{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python для Анализа Данных\n",
    "\n",
    "# Лекция 6: Обработка данных с Pandas I\n",
    "\n",
    "**Автор** Полина Полунина\n",
    "\n",
    "По материалам Элен Теванян \n",
    "\n",
    "**tg:** @ppolunina"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas. Загрузка библиотек\n",
    "\n",
    " -  <a href=\"http://pandas.pydata.org/\">Pandas</a> - библиотека для обработки и анализа данных. Предназначена для данных разной природы - матричных, панельных данных, временных рядов. Претендует на звание самого мощного и гибкого средства для анализа данных с открытым исходным кодом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В пандас есть две структуры данных:\n",
    "- Series: одномерный массив с именованными индексами (чаще всего, данные одного типа)\n",
    "- DataFrame: двухмерный массив, имеет табличную структуру, легко изменяется по размерам, может содержать в себе данные разных типов\n",
    "\n",
    "Оба типа можно создавать вручную с помощью функций из самой библиотеки:\n",
    "- pandas.Series(data=None, index=None, dtype=None)\n",
    "- pandas.DataFrame(data=None, index=None, columns=None, dtype=None)\n",
    "\n",
    "- **data** - данные, которые надо записать в структуру\n",
    "- **index** - индексы строк\n",
    "- **columns** - названия столбцов\n",
    "- **dtype** - тип данных\n",
    "\n",
    "Кроме data, остальные параметры опциональны\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы, конечно, можем сами создавать датафреймы!\n",
    "\n",
    "Например, кто-то нашел нам кусок данных и просит воспроизвести этот датасет:\n",
    "\n",
    "<img src=\"https://i.imgur.com/FUCGiKP.png\">\n",
    "\n",
    "Давайте разберемся, что здесь, что и запишем в известную нам конструкцию - листы. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['country', 'province', 'region_1', 'region_2']\n",
    "index = [0, 1, 10, 100]\n",
    "data = [['Italy', 'Sicily & Sardinia', 'Etna', 'NaN'], \n",
    "        ['Portugal', 'Douro', 'NaN', 'NaN'],\n",
    "       ['US', 'California', 'Napa Valley', 'Napa'],\n",
    "       ['US', 'New York', 'Finger Lakes', 'Finger Lakes']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А теперь соберем в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Sicily &amp; Sardinia</td>\n",
       "      <td>Etna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Douro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>US</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>US</td>\n",
       "      <td>New York</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Finger Lakes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country           province      region_1      region_2\n",
       "0       Italy  Sicily & Sardinia          Etna           NaN\n",
       "1    Portugal              Douro           NaN           NaN\n",
       "10         US         California   Napa Valley          Napa\n",
       "100        US           New York  Finger Lakes  Finger Lakes"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data, columns = columns, index = index)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка и запись данных\n",
    "\n",
    "Правда в том, что мы не будем так жестоко к себе и вручную вбивать данные не будем. А будем загружать из файла. \n",
    "\n",
    "\n",
    "- Функции типа **pd.read_формат** и **pd.to_формат**\n",
    "считывают и записывают данные соответственно. <br /> Полный список можно найти в документации:\n",
    "http://pandas.pydata.org/pandas-docs/stable/io.html\n",
    "\n",
    "Я лично перестала пользоваться экселем даже для беглого смотра данных, Pandas грузит гигабайтные файлы в худшем случае минуту-полторы. \n",
    "\n",
    "Научимся считывать данные в формате csv (comma separated value) функцией:\n",
    "\n",
    "- <a href=\"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv\"> pd.read_csv()</a>: \n",
    "\n",
    "Аргументов у нее очень много, критически важные:\n",
    " - **filepath_or_buffer** - текстовая строка с названием (адресом) файла\n",
    " - **sep** - разделитель между данными\n",
    " - **header** - номер строки, в которой в файле указаны названия столбцов, None, если нет\n",
    " - **names** - список с названиями колонок\n",
    " - **index_col** - или номер столбца, или список,  или ничего - колонка, из которой надо взять названия строк\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dpo_1-2_winemag-data_first150k.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Смотрим, что загрузилось"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 country                                        description  \\\n",
       "0           0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1           1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2           2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3           3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4           4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что-то не то с первым столбцом, немного поправим"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dpo_1-2_winemag-data_first150k.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "\n",
       "         region_1 region_2             variety                   winery  \n",
       "0     Napa Valley     Napa  Cabernet Sauvignon                    Heitz  \n",
       "1            Toro      NaN       Tinta de Toro  Bodega Carmen Rodríguez  \n",
       "2  Knights Valley   Sonoma     Sauvignon Blanc                 Macauley  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Смотрим, что загрузилось:\n",
    "\n",
    "- Посчитаем, сколько записей\n",
    "- Посмотрим, какого типа данные\n",
    "- Проверим, есть ли пропуски"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем, сколько записей в данных.\n",
    "\n",
    "- Помогает метод **count()**. Это значит, что к любому датафрейму стучимся в гости с этим методом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country        150925\n",
       "description    150930\n",
       "designation    105195\n",
       "points         150930\n",
       "price          137235\n",
       "province       150925\n",
       "region_1       125870\n",
       "region_2        60953\n",
       "variety        150930\n",
       "winery         150930\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150930, 10)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Метод info() заодно показывает, какого типа данные в столбцах"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150930 entries, 0 to 150929\n",
      "Data columns (total 10 columns):\n",
      "country        150925 non-null object\n",
      "description    150930 non-null object\n",
      "designation    105195 non-null object\n",
      "points         150930 non-null int64\n",
      "price          137235 non-null float64\n",
      "province       150925 non-null object\n",
      "region_1       125870 non-null object\n",
      "region_2       60953 non-null object\n",
      "variety        150930 non-null object\n",
      "winery         150930 non-null object\n",
      "dtypes: float64(1), int64(1), object(8)\n",
      "memory usage: 12.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country         object\n",
       "description     object\n",
       "designation     object\n",
       "points           int64\n",
       "price          float64\n",
       "province        object\n",
       "region_1        object\n",
       "region_2        object\n",
       "variety         object\n",
       "winery          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начнем проверять на пропуски! \n",
    "\n",
    "- .isnull() - выдает табличку, где False - ячейка заполнена, True - ячейка пуста :( Ближайшая родня - isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11560127211290001"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum() / data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11560127211290001"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum().sum() / data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Немного опережу события и покажу, как оценить масштаб бедствия визуально. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c8d9afd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "sns_heatmap = sns.heatmap(data.isnull(), yticklabels=False, cbar=False, cmap='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что с ним делать?\n",
    "\n",
    "Выбора не очень много: <br>\n",
    "\n",
    "1) Удалять: \n",
    "- dropna(axis=0, how='any'): axis = 0 - удаляем построчно, axis = 1 выкидываем столбец; how ='any' - выкидываем, если есть хотя бы одна ячейка пустая. how = 'all' - выкидываем, если есть полностью пустая строка или столбец\n",
    "\n",
    "2) Вставлять информацию самим:\n",
    "- fillna() - это отдельное искусство, как заполнять. \n",
    "\n",
    "\n",
    "Пока не будем трогать данные. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Описательные статистики\n",
    "\n",
    "Теперь посмотрим, а что содержательно у нас есть на руках. \n",
    "\n",
    "Глазами просматривать не будем, а попросим посчитать основные описательные статистики. Причем сразу все :) \n",
    "\n",
    "- describe() - метод, который возвращает табличку с описательными статистиками. В таком виде считает все для числовых столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150930.000000</td>\n",
       "      <td>137235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.888418</td>\n",
       "      <td>33.131482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.222392</td>\n",
       "      <td>36.322536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              points          price\n",
       "count  150930.000000  137235.000000\n",
       "mean       87.888418      33.131482\n",
       "std         3.222392      36.322536\n",
       "min        80.000000       4.000000\n",
       "25%        86.000000      16.000000\n",
       "50%        88.000000      24.000000\n",
       "75%        90.000000      40.000000\n",
       "max       100.000000    2300.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Немножко магии, и для нечисловых данные тоже будут свои описательные статистики. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150925</td>\n",
       "      <td>150930</td>\n",
       "      <td>105195</td>\n",
       "      <td>150925</td>\n",
       "      <td>125870</td>\n",
       "      <td>60953</td>\n",
       "      <td>150930</td>\n",
       "      <td>150930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>48</td>\n",
       "      <td>97821</td>\n",
       "      <td>30621</td>\n",
       "      <td>455</td>\n",
       "      <td>1236</td>\n",
       "      <td>18</td>\n",
       "      <td>632</td>\n",
       "      <td>14810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>US</td>\n",
       "      <td>A little bit funky and unsettled when you pop ...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Williams Selyem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>62397</td>\n",
       "      <td>6</td>\n",
       "      <td>2752</td>\n",
       "      <td>44508</td>\n",
       "      <td>6209</td>\n",
       "      <td>13057</td>\n",
       "      <td>14482</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country                                        description designation  \\\n",
       "count   150925                                             150930      105195   \n",
       "unique      48                                              97821       30621   \n",
       "top         US  A little bit funky and unsettled when you pop ...     Reserve   \n",
       "freq     62397                                                  6        2752   \n",
       "\n",
       "          province     region_1       region_2     variety           winery  \n",
       "count       150925       125870          60953      150930           150930  \n",
       "unique         455         1236             18         632            14810  \n",
       "top     California  Napa Valley  Central Coast  Chardonnay  Williams Selyem  \n",
       "freq         44508         6209          13057       14482              374  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Срезы данных\n",
    "\n",
    "Допустим, нам не нужен датасет, а только определенные столбцы или строки или столбцы и строки. \n",
    "\n",
    "\n",
    "Как делать?\n",
    "Помним, что:\n",
    "- у столбцов есть названия\n",
    "- у строк есть названия\n",
    "- если нет названий, то они пронумерованы с нуля\n",
    "\n",
    "Основываясь на этой идее, мы начнем отбирать данные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "\n",
       "         designation  points  price    province     region_1 region_2  \\\n",
       "0  Martha's Vineyard      96  235.0  California  Napa Valley     Napa   \n",
       "\n",
       "              variety winery  \n",
       "0  Cabernet Sauvignon  Heitz  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбираем по столбцам. Версия 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    235.0\n",
       "1    110.0\n",
       "2     90.0\n",
       "3     65.0\n",
       "4     66.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['price'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.0</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price country\n",
       "0  235.0      US\n",
       "1  110.0   Spain\n",
       "2   90.0      US\n",
       "3   65.0      US\n",
       "4   66.0  France"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['price','country']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбираем по  строкам. Версия 1. \n",
    "\n",
    "Были бы названия - вместо цифр подставили бы названия и все вышло бы также :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Deep, dense and pure from the opening bell, th...</td>\n",
       "      <td>Numanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Elegance, complexity and structure come togeth...</td>\n",
       "      <td>Ronco della Chiesa</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>Collio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friulano</td>\n",
       "      <td>Borgo del Tiglio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US</td>\n",
       "      <td>First made in 2006, this succulent luscious Ch...</td>\n",
       "      <td>Sigrid</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>US</td>\n",
       "      <td>Heitz has made this stellar rosé from the rare...</td>\n",
       "      <td>Grignolino</td>\n",
       "      <td>95</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Rosé</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Yields were down in 2015, but intensity is up,...</td>\n",
       "      <td>Maté's Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Kumeu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Kumeu River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>This Bulgarian Mavrud presents the nose with s...</td>\n",
       "      <td>Bergulé</td>\n",
       "      <td>90</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mavrud</td>\n",
       "      <td>Villa Melnik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Forest floor, tilled soil, mature berry and a ...</td>\n",
       "      <td>Riserva</td>\n",
       "      <td>90</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Brunello di Montalcino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sangiovese</td>\n",
       "      <td>Carillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Earthy plum and cherry aromas score points for...</td>\n",
       "      <td>Amandi</td>\n",
       "      <td>90</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Galicia</td>\n",
       "      <td>Ribeira Sacra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mencía</td>\n",
       "      <td>Don Bernardino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Italy</td>\n",
       "      <td>A blend of 90% Sangiovese and 10% Canaiolo, th...</td>\n",
       "      <td>Vigneto Odoardo Beccari Riserva</td>\n",
       "      <td>90</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Chianti Classico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Red Blend</td>\n",
       "      <td>Vignavecchia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Italy</td>\n",
       "      <td>This robust red opens with aromas of espresso,...</td>\n",
       "      <td>Riserva</td>\n",
       "      <td>90</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Brunello di Montalcino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sangiovese</td>\n",
       "      <td>Capanne Ricci</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>US</td>\n",
       "      <td>A blend of Cabernet from Grand Ciel (31%), Cie...</td>\n",
       "      <td>Four Flags</td>\n",
       "      <td>90</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Red Mountain</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>DeLille</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Concentrated, ripe blackberry and cassis aroma...</td>\n",
       "      <td>The Apple Doesn't Fall Far From The Tree</td>\n",
       "      <td>91</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Mendoza Province</td>\n",
       "      <td>Mendoza</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Matias Riccitelli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>US</td>\n",
       "      <td>Fresh boysenberries and a blueberry sorbet cha...</td>\n",
       "      <td>Estate Select</td>\n",
       "      <td>91</td>\n",
       "      <td>36.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Clara Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Syrah</td>\n",
       "      <td>Jason-Stephens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>US</td>\n",
       "      <td>Sweetened tannins highlight a depth of chocola...</td>\n",
       "      <td>District Collection</td>\n",
       "      <td>91</td>\n",
       "      <td>85.0</td>\n",
       "      <td>California</td>\n",
       "      <td>St. Helena</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Raymond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>US</td>\n",
       "      <td>An elegant blend from different estate vineyar...</td>\n",
       "      <td>Premier Cuvée</td>\n",
       "      <td>91</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Archery Summit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>US</td>\n",
       "      <td>Generous black-cherry fruit anchors this barre...</td>\n",
       "      <td>Aeolian</td>\n",
       "      <td>91</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Eola-Amity Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bethel Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas of French oak, coconut, vanilla and spi...</td>\n",
       "      <td>Bricco Gattera</td>\n",
       "      <td>91</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Piedmont</td>\n",
       "      <td>Barolo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nebbiolo</td>\n",
       "      <td>Cordero di Montezemolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>US</td>\n",
       "      <td>Bright, light oak shadings dress up this mediu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>10.0</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California Other</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Belle Ambiance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>France</td>\n",
       "      <td>This is a smooth, soft wine that is full of bl...</td>\n",
       "      <td>Château Beauvillain-Monpezat</td>\n",
       "      <td>86</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Cahors</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec-Merlot</td>\n",
       "      <td>Rigal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>US</td>\n",
       "      <td>Juicy kiwi, lime blossom and sour apple candy ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>South Coast</td>\n",
       "      <td>South Coast</td>\n",
       "      <td>Viognier</td>\n",
       "      <td>Hawk Watch Winery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>US</td>\n",
       "      <td>Aromas of ripe (leaning almost overripe) apple...</td>\n",
       "      <td>Tudor Hills Vineyard</td>\n",
       "      <td>86</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Yakima Valley</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Pinot Grigio</td>\n",
       "      <td>Martinez &amp; Martinez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>This state-owned estate of forests and vineyar...</td>\n",
       "      <td>Companhia das Lezírias Herdade de Catapereiro ...</td>\n",
       "      <td>86</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Tejo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Wines &amp; Winemakers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Aromas of prune, raisin and black plum are ful...</td>\n",
       "      <td>Reserva</td>\n",
       "      <td>86</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Mendoza Province</td>\n",
       "      <td>Valle de Uco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Viñalba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>US</td>\n",
       "      <td>Let this burly, big-boned beauty open to fully...</td>\n",
       "      <td>The Flyer</td>\n",
       "      <td>91</td>\n",
       "      <td>59.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Russian River Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>MacPhail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>US</td>\n",
       "      <td>This medium-bodied wine has a compelling blend...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>20.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Navarro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Juicy and delicious, this has aromas of ripe o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Southern Italy</td>\n",
       "      <td>Greco di Tufo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greco</td>\n",
       "      <td>Cantine di Marzo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>US</td>\n",
       "      <td>Quite dark in color, brooding aromas of olalli...</td>\n",
       "      <td>Estate</td>\n",
       "      <td>90</td>\n",
       "      <td>42.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Adelaida District</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Grenache</td>\n",
       "      <td>Brecon Estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>France</td>\n",
       "      <td>Lightly structured, this is a balanced, ripe w...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Bordeaux Rosé</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rosé</td>\n",
       "      <td>Château Suau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>US</td>\n",
       "      <td>Red-cherry and strawberry aromas meld with ric...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>55.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Goldeneye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150780</th>\n",
       "      <td>US</td>\n",
       "      <td>Very much in the vein of a super-oaked, ripe C...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Chalone</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Blanc</td>\n",
       "      <td>Chalone Vineyard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150785</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Rich and complex from the start, the nose and ...</td>\n",
       "      <td>Reserva de la Familia</td>\n",
       "      <td>90</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Santa Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150790</th>\n",
       "      <td>US</td>\n",
       "      <td>Always a bit herbaceous, this year the underly...</td>\n",
       "      <td>Meritage</td>\n",
       "      <td>90</td>\n",
       "      <td>28.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "      <td>Dry Creek Vineyard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150795</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Mild tropical fruit and citrus aromas draw you...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Colchagua Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Dallas Conté</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150800</th>\n",
       "      <td>US</td>\n",
       "      <td>You don't get sun-ripened Merlot from Monterey...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>16.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Monterey</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Ventana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150805</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Bacon and lavender are prominent on the meaty,...</td>\n",
       "      <td>Private Reserve Don Melchor</td>\n",
       "      <td>88</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Concha y Toro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150810</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Deep cassis aromas, a touch of licorice, and m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>11.0</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Barossa Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Jacob's Creek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150815</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Partial barrel-fermentation shows in this wine...</td>\n",
       "      <td>Terrunyo</td>\n",
       "      <td>88</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Casablanca Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Concha y Toro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150820</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Sure, cedar and chocolate are the dominant aro...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>87</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Santa Ema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150825</th>\n",
       "      <td>US</td>\n",
       "      <td>Still dependable despite changes in ownership,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>22.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>William Hill Estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150830</th>\n",
       "      <td>Chile</td>\n",
       "      <td>A refreshing wine, with nicely ripened fruit a...</td>\n",
       "      <td>La Escultura</td>\n",
       "      <td>87</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Casablanca Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Errazuriz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150835</th>\n",
       "      <td>US</td>\n",
       "      <td>A little earthy, with plummy aromas accompanie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>28.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Arroyo Grande Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Talley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150840</th>\n",
       "      <td>Chile</td>\n",
       "      <td>At this price, you can expect an honest, workm...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Central Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Caliterra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150845</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Richly fruity and soft, this wine boasts loads...</td>\n",
       "      <td>Xplorador</td>\n",
       "      <td>85</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Rapel Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Concha y Toro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150850</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Bright grapefruit and gooseberry aromas flesh ...</td>\n",
       "      <td>Santa Digna</td>\n",
       "      <td>85</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Curicó Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Miguel Torres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150855</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Opening with a bouquet of red berries compleme...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Rapel Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>La Palma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150860</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Not unlike this producer's 1999 Chardonnay Sel...</td>\n",
       "      <td>Gran Reserva</td>\n",
       "      <td>84</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Colchagua Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Château La Joya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150865</th>\n",
       "      <td>US</td>\n",
       "      <td>Starts out with Cabernet-like aromas of black ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>10.0</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California Other</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Compass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150870</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Offers varietally correct flavors of black cur...</td>\n",
       "      <td>Xplorador</td>\n",
       "      <td>83</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Concha y Toro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150875</th>\n",
       "      <td>US</td>\n",
       "      <td>From the folks who invented white Zinfandel, a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California Other</td>\n",
       "      <td>Zinfandel</td>\n",
       "      <td>Sutter Home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150880</th>\n",
       "      <td>Chile</td>\n",
       "      <td>A pleasant and cleanly made quaffing wine with...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Santa Ema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150885</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Orangey aromas wrapped in not very subtle oak ...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>83</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Santa Ema</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150890</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Wearing a heavy mantle of rather green, not-to...</td>\n",
       "      <td>Reserva</td>\n",
       "      <td>82</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Undurraga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150895</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Strawberry and wet hay aromas give way to a sw...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Casablanca Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Veramonte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150900</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Aromas of freshly cut lumber, complete with so...</td>\n",
       "      <td>Prima Reserva</td>\n",
       "      <td>81</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>De Martino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150905</th>\n",
       "      <td>Chile</td>\n",
       "      <td>There's not much point in making a reserve-sty...</td>\n",
       "      <td>Prima Reserva</td>\n",
       "      <td>80</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>De Martino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150910</th>\n",
       "      <td>France</td>\n",
       "      <td>Scents of graham cracker and malted milk choco...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chambolle-Musigny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150915</th>\n",
       "      <td>US</td>\n",
       "      <td>Decades ago, Beringer’s then-winemaker Myron N...</td>\n",
       "      <td>Nightingale</td>\n",
       "      <td>93</td>\n",
       "      <td>30.0</td>\n",
       "      <td>California</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Beringer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150920</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Rich and mature aromas of smoke, earth and her...</td>\n",
       "      <td>Brut Riserva</td>\n",
       "      <td>91</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>Trento</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Letrari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150925</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Many people feel Fiano represents southern Ita...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Southern Italy</td>\n",
       "      <td>Fiano di Avellino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Feudi di San Gregorio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30186 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            country                                        description  \\\n",
       "0                US  This tremendous 100% varietal wine hails from ...   \n",
       "5             Spain  Deep, dense and pure from the opening bell, th...   \n",
       "10            Italy  Elegance, complexity and structure come togeth...   \n",
       "15               US  First made in 2006, this succulent luscious Ch...   \n",
       "20               US  Heitz has made this stellar rosé from the rare...   \n",
       "25      New Zealand  Yields were down in 2015, but intensity is up,...   \n",
       "30         Bulgaria  This Bulgarian Mavrud presents the nose with s...   \n",
       "35            Italy  Forest floor, tilled soil, mature berry and a ...   \n",
       "40            Spain  Earthy plum and cherry aromas score points for...   \n",
       "45            Italy  A blend of 90% Sangiovese and 10% Canaiolo, th...   \n",
       "50            Italy  This robust red opens with aromas of espresso,...   \n",
       "55               US  A blend of Cabernet from Grand Ciel (31%), Cie...   \n",
       "60        Argentina  Concentrated, ripe blackberry and cassis aroma...   \n",
       "65               US  Fresh boysenberries and a blueberry sorbet cha...   \n",
       "70               US  Sweetened tannins highlight a depth of chocola...   \n",
       "75               US  An elegant blend from different estate vineyar...   \n",
       "80               US  Generous black-cherry fruit anchors this barre...   \n",
       "85            Italy  Aromas of French oak, coconut, vanilla and spi...   \n",
       "90               US  Bright, light oak shadings dress up this mediu...   \n",
       "95           France  This is a smooth, soft wine that is full of bl...   \n",
       "100              US  Juicy kiwi, lime blossom and sour apple candy ...   \n",
       "105              US  Aromas of ripe (leaning almost overripe) apple...   \n",
       "110        Portugal  This state-owned estate of forests and vineyar...   \n",
       "115       Argentina  Aromas of prune, raisin and black plum are ful...   \n",
       "120              US  Let this burly, big-boned beauty open to fully...   \n",
       "125              US  This medium-bodied wine has a compelling blend...   \n",
       "130           Italy  Juicy and delicious, this has aromas of ripe o...   \n",
       "135              US  Quite dark in color, brooding aromas of olalli...   \n",
       "140          France  Lightly structured, this is a balanced, ripe w...   \n",
       "145              US  Red-cherry and strawberry aromas meld with ric...   \n",
       "...             ...                                                ...   \n",
       "150780           US  Very much in the vein of a super-oaked, ripe C...   \n",
       "150785        Chile  Rich and complex from the start, the nose and ...   \n",
       "150790           US  Always a bit herbaceous, this year the underly...   \n",
       "150795        Chile  Mild tropical fruit and citrus aromas draw you...   \n",
       "150800           US  You don't get sun-ripened Merlot from Monterey...   \n",
       "150805        Chile  Bacon and lavender are prominent on the meaty,...   \n",
       "150810    Australia  Deep cassis aromas, a touch of licorice, and m...   \n",
       "150815        Chile  Partial barrel-fermentation shows in this wine...   \n",
       "150820        Chile  Sure, cedar and chocolate are the dominant aro...   \n",
       "150825           US  Still dependable despite changes in ownership,...   \n",
       "150830        Chile  A refreshing wine, with nicely ripened fruit a...   \n",
       "150835           US  A little earthy, with plummy aromas accompanie...   \n",
       "150840        Chile  At this price, you can expect an honest, workm...   \n",
       "150845        Chile  Richly fruity and soft, this wine boasts loads...   \n",
       "150850        Chile  Bright grapefruit and gooseberry aromas flesh ...   \n",
       "150855        Chile  Opening with a bouquet of red berries compleme...   \n",
       "150860        Chile  Not unlike this producer's 1999 Chardonnay Sel...   \n",
       "150865           US  Starts out with Cabernet-like aromas of black ...   \n",
       "150870        Chile  Offers varietally correct flavors of black cur...   \n",
       "150875           US  From the folks who invented white Zinfandel, a...   \n",
       "150880        Chile  A pleasant and cleanly made quaffing wine with...   \n",
       "150885        Chile  Orangey aromas wrapped in not very subtle oak ...   \n",
       "150890        Chile  Wearing a heavy mantle of rather green, not-to...   \n",
       "150895        Chile  Strawberry and wet hay aromas give way to a sw...   \n",
       "150900        Chile  Aromas of freshly cut lumber, complete with so...   \n",
       "150905        Chile  There's not much point in making a reserve-sty...   \n",
       "150910       France  Scents of graham cracker and malted milk choco...   \n",
       "150915           US  Decades ago, Beringer’s then-winemaker Myron N...   \n",
       "150920        Italy  Rich and mature aromas of smoke, earth and her...   \n",
       "150925        Italy  Many people feel Fiano represents southern Ita...   \n",
       "\n",
       "                                              designation  points  price  \\\n",
       "0                                       Martha's Vineyard      96  235.0   \n",
       "5                                               Numanthia      95   73.0   \n",
       "10                                     Ronco della Chiesa      95   80.0   \n",
       "15                                                 Sigrid      95   90.0   \n",
       "20                                             Grignolino      95   24.0   \n",
       "25                                        Maté's Vineyard      94   57.0   \n",
       "30                                                Bergulé      90   15.0   \n",
       "35                                                Riserva      90  135.0   \n",
       "40                                                 Amandi      90   17.0   \n",
       "45                        Vigneto Odoardo Beccari Riserva      90   30.0   \n",
       "50                                                Riserva      90  100.0   \n",
       "55                                             Four Flags      90   69.0   \n",
       "60               The Apple Doesn't Fall Far From The Tree      91   30.0   \n",
       "65                                          Estate Select      91   36.0   \n",
       "70                                    District Collection      91   85.0   \n",
       "75                                          Premier Cuvée      91   54.0   \n",
       "80                                                Aeolian      91   42.0   \n",
       "85                                         Bricco Gattera      91   80.0   \n",
       "90                                                    NaN      86   10.0   \n",
       "95                           Château Beauvillain-Monpezat      86   14.0   \n",
       "100                                                   NaN      86   24.0   \n",
       "105                                  Tudor Hills Vineyard      86   17.0   \n",
       "110     Companhia das Lezírias Herdade de Catapereiro ...      86   12.0   \n",
       "115                                               Reserva      86   15.0   \n",
       "120                                             The Flyer      91   59.0   \n",
       "125                                                   NaN      91   20.0   \n",
       "130                                                   NaN      90   18.0   \n",
       "135                                                Estate      90   42.0   \n",
       "140                                                   NaN      90   15.0   \n",
       "145                                                   NaN      90   55.0   \n",
       "...                                                   ...     ...    ...   \n",
       "150780                                                NaN      91   24.0   \n",
       "150785                              Reserva de la Familia      90   15.0   \n",
       "150790                                           Meritage      90   28.0   \n",
       "150795                                                NaN      89   10.0   \n",
       "150800                                                NaN      89   16.0   \n",
       "150805                        Private Reserve Don Melchor      88   40.0   \n",
       "150810                                                NaN      88   11.0   \n",
       "150815                                           Terrunyo      88   29.0   \n",
       "150820                                            Reserve      87   14.0   \n",
       "150825                                                NaN      87   22.0   \n",
       "150830                                       La Escultura      87   10.0   \n",
       "150835                                                NaN      86   28.0   \n",
       "150840                                                NaN      86    8.0   \n",
       "150845                                          Xplorador      85    8.0   \n",
       "150850                                        Santa Digna      85   12.0   \n",
       "150855                                                NaN      85    8.0   \n",
       "150860                                       Gran Reserva      84   15.0   \n",
       "150865                                                NaN      84   10.0   \n",
       "150870                                          Xplorador      83    8.0   \n",
       "150875                                                NaN      83    6.0   \n",
       "150880                                                NaN      83    9.0   \n",
       "150885                                            Reserve      83   14.0   \n",
       "150890                                            Reserva      82   12.0   \n",
       "150895                                                NaN      82   10.0   \n",
       "150900                                      Prima Reserva      81   13.0   \n",
       "150905                                      Prima Reserva      80   13.0   \n",
       "150910                                                NaN      89   38.0   \n",
       "150915                                        Nightingale      93   30.0   \n",
       "150920                                       Brut Riserva      91   19.0   \n",
       "150925                                                NaN      91   20.0   \n",
       "\n",
       "                  province                region_1                 region_2  \\\n",
       "0               California             Napa Valley                     Napa   \n",
       "5           Northern Spain                    Toro                      NaN   \n",
       "10      Northeastern Italy                  Collio                      NaN   \n",
       "15                  Oregon       Willamette Valley        Willamette Valley   \n",
       "20              California             Napa Valley                     Napa   \n",
       "25                   Kumeu                     NaN                      NaN   \n",
       "30                Bulgaria                     NaN                      NaN   \n",
       "35                 Tuscany  Brunello di Montalcino                      NaN   \n",
       "40                 Galicia           Ribeira Sacra                      NaN   \n",
       "45                 Tuscany        Chianti Classico                      NaN   \n",
       "50                 Tuscany  Brunello di Montalcino                      NaN   \n",
       "55              Washington            Red Mountain          Columbia Valley   \n",
       "60        Mendoza Province                 Mendoza                      NaN   \n",
       "65              California      Santa Clara Valley            Central Coast   \n",
       "70              California              St. Helena                     Napa   \n",
       "75                  Oregon       Willamette Valley        Willamette Valley   \n",
       "80                  Oregon        Eola-Amity Hills        Willamette Valley   \n",
       "85                Piedmont                  Barolo                      NaN   \n",
       "90              California              California         California Other   \n",
       "95        Southwest France                  Cahors                      NaN   \n",
       "100             California             South Coast              South Coast   \n",
       "105             Washington           Yakima Valley          Columbia Valley   \n",
       "110                   Tejo                     NaN                      NaN   \n",
       "115       Mendoza Province            Valle de Uco                      NaN   \n",
       "120             California    Russian River Valley                   Sonoma   \n",
       "125             California         Anderson Valley  Mendocino/Lake Counties   \n",
       "130         Southern Italy           Greco di Tufo                      NaN   \n",
       "135             California       Adelaida District            Central Coast   \n",
       "140               Bordeaux           Bordeaux Rosé                      NaN   \n",
       "145             California         Anderson Valley  Mendocino/Lake Counties   \n",
       "...                    ...                     ...                      ...   \n",
       "150780          California                 Chalone            Central Coast   \n",
       "150785        Maipo Valley                     NaN                      NaN   \n",
       "150790          California           Sonoma County                   Sonoma   \n",
       "150795    Colchagua Valley                     NaN                      NaN   \n",
       "150800          California                Monterey            Central Coast   \n",
       "150805        Maipo Valley                     NaN                      NaN   \n",
       "150810     South Australia          Barossa Valley                      NaN   \n",
       "150815   Casablanca Valley                     NaN                      NaN   \n",
       "150820        Maipo Valley                     NaN                      NaN   \n",
       "150825          California             Napa Valley                     Napa   \n",
       "150830   Casablanca Valley                     NaN                      NaN   \n",
       "150835          California    Arroyo Grande Valley            Central Coast   \n",
       "150840      Central Valley                     NaN                      NaN   \n",
       "150845        Rapel Valley                     NaN                      NaN   \n",
       "150850       Curicó Valley                     NaN                      NaN   \n",
       "150855        Rapel Valley                     NaN                      NaN   \n",
       "150860    Colchagua Valley                     NaN                      NaN   \n",
       "150865          California              California         California Other   \n",
       "150870        Maipo Valley                     NaN                      NaN   \n",
       "150875          California              California         California Other   \n",
       "150880        Maipo Valley                     NaN                      NaN   \n",
       "150885        Maipo Valley                     NaN                      NaN   \n",
       "150890        Maipo Valley                     NaN                      NaN   \n",
       "150895   Casablanca Valley                     NaN                      NaN   \n",
       "150900        Maipo Valley                     NaN                      NaN   \n",
       "150905        Maipo Valley                     NaN                      NaN   \n",
       "150910            Burgundy       Chambolle-Musigny                      NaN   \n",
       "150915          California             North Coast              North Coast   \n",
       "150920  Northeastern Italy                  Trento                      NaN   \n",
       "150925      Southern Italy       Fiano di Avellino                      NaN   \n",
       "\n",
       "                         variety                  winery  \n",
       "0             Cabernet Sauvignon                   Heitz  \n",
       "5                  Tinta de Toro               Numanthia  \n",
       "10                      Friulano        Borgo del Tiglio  \n",
       "15                    Chardonnay               Bergström  \n",
       "20                          Rosé                   Heitz  \n",
       "25                    Chardonnay             Kumeu River  \n",
       "30                        Mavrud            Villa Melnik  \n",
       "35                    Sangiovese                Carillon  \n",
       "40                        Mencía          Don Bernardino  \n",
       "45                     Red Blend            Vignavecchia  \n",
       "50                    Sangiovese           Capanne Ricci  \n",
       "55            Cabernet Sauvignon                 DeLille  \n",
       "60                        Malbec       Matias Riccitelli  \n",
       "65                         Syrah          Jason-Stephens  \n",
       "70            Cabernet Sauvignon                 Raymond  \n",
       "75                    Pinot Noir          Archery Summit  \n",
       "80                    Pinot Noir          Bethel Heights  \n",
       "85                      Nebbiolo  Cordero di Montezemolo  \n",
       "90            Cabernet Sauvignon          Belle Ambiance  \n",
       "95                 Malbec-Merlot                   Rigal  \n",
       "100                     Viognier       Hawk Watch Winery  \n",
       "105                 Pinot Grigio     Martinez & Martinez  \n",
       "110               Portuguese Red      Wines & Winemakers  \n",
       "115                       Malbec                 Viñalba  \n",
       "120                   Pinot Noir                MacPhail  \n",
       "125                   Pinot Noir                 Navarro  \n",
       "130                        Greco        Cantine di Marzo  \n",
       "135                     Grenache           Brecon Estate  \n",
       "140                         Rosé            Château Suau  \n",
       "145                   Pinot Noir               Goldeneye  \n",
       "...                          ...                     ...  \n",
       "150780               Pinot Blanc        Chalone Vineyard  \n",
       "150785                Chardonnay          Santa Carolina  \n",
       "150790  Bordeaux-style Red Blend      Dry Creek Vineyard  \n",
       "150795                Chardonnay            Dallas Conté  \n",
       "150800                    Merlot                 Ventana  \n",
       "150805        Cabernet Sauvignon           Concha y Toro  \n",
       "150810        Cabernet Sauvignon           Jacob's Creek  \n",
       "150815           Sauvignon Blanc           Concha y Toro  \n",
       "150820                    Merlot               Santa Ema  \n",
       "150825        Cabernet Sauvignon     William Hill Estate  \n",
       "150830           Sauvignon Blanc               Errazuriz  \n",
       "150835                Pinot Noir                  Talley  \n",
       "150840                Chardonnay               Caliterra  \n",
       "150845                    Merlot           Concha y Toro  \n",
       "150850           Sauvignon Blanc           Miguel Torres  \n",
       "150855                    Merlot                La Palma  \n",
       "150860                Chardonnay         Château La Joya  \n",
       "150865                    Merlot                 Compass  \n",
       "150870        Cabernet Sauvignon           Concha y Toro  \n",
       "150875                 Zinfandel             Sutter Home  \n",
       "150880                    Merlot               Santa Ema  \n",
       "150885                Chardonnay               Santa Ema  \n",
       "150890                Chardonnay               Undurraga  \n",
       "150895                    Merlot               Veramonte  \n",
       "150900        Cabernet Sauvignon              De Martino  \n",
       "150905                    Merlot              De Martino  \n",
       "150910                Pinot Noir             Michel Gros  \n",
       "150915               White Blend                Beringer  \n",
       "150920           Champagne Blend                 Letrari  \n",
       "150925               White Blend   Feudi di San Gregorio  \n",
       "\n",
       "[30186 rows x 10 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data[::5]"
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "data[:1]"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбор по столбцам. Версия 2. Все еще по названиям "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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       "      <th>price</th>\n",
       "      <th>points</th>\n",
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       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>73.0</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>65.0</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.0</td>\n",
       "      <td>95</td>\n",
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       "   price  points\n",
       "4   66.0      95\n",
       "5   73.0      95\n",
       "6   65.0      95\n",
       "7  110.0      95"
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   "source": [
    "#### Отбор по  строкам. Версия 2. Все еще по названиям "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Deep, dense and pure from the opening bell, th...</td>\n",
       "      <td>Numanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "5   Spain  Deep, dense and pure from the opening bell, th...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "5                             Numanthia      95   73.0  Northern Spain   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "5               Toro                NaN       Tinta de Toro   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  \n",
       "5                Numanthia  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбор по строчкам и столбцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[0:5,['price', 'country']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбор по строчкам и столбцам. Версия 3. По номеру строк и столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>region_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>Napa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>California</td>\n",
       "      <td>Napa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              province           region_2\n",
       "0           California               Napa\n",
       "5       Northern Spain                NaN\n",
       "10  Northeastern Italy                NaN\n",
       "15              Oregon  Willamette Valley\n",
       "20          California               Napa"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[::5, [5,7]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Отбор с условиями\n",
    "\n",
    "Так, а если мне нужны вина дороже $15 долларов? Как быть?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#задаем маску\n",
    "mask = data['price'] > 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Deep, dense and pure from the opening bell, th...</td>\n",
       "      <td>Numanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Slightly gritty black-fruit aromas include a s...</td>\n",
       "      <td>San Román</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Maurodos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Lush cedary black-fruit aromas are luxe and of...</td>\n",
       "      <td>Carodorum Único Crianza</td>\n",
       "      <td>95</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>US</td>\n",
       "      <td>This re-named vineyard was formerly bottled as...</td>\n",
       "      <td>Silice</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Chehalem Mountains</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US</td>\n",
       "      <td>The producer sources from two blocks of the vi...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Blue Farm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Elegance, complexity and structure come togeth...</td>\n",
       "      <td>Ronco della Chiesa</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>Collio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friulano</td>\n",
       "      <td>Borgo del Tiglio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>US</td>\n",
       "      <td>From 18-year-old vines, this supple well-balan...</td>\n",
       "      <td>Estate Vineyard Wadensvil Block</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Ribbon Ridge</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>US</td>\n",
       "      <td>A standout even in this terrific lineup of 201...</td>\n",
       "      <td>Weber Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>This wine is in peak condition. The tannins an...</td>\n",
       "      <td>Château Montus Prestige</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Madiran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tannat</td>\n",
       "      <td>Vignobles Brumont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>US</td>\n",
       "      <td>With its sophisticated mix of mineral, acid an...</td>\n",
       "      <td>Grace Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Domaine Serene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US</td>\n",
       "      <td>First made in 2006, this succulent luscious Ch...</td>\n",
       "      <td>Sigrid</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>US</td>\n",
       "      <td>This blockbuster, powerhouse of a wine suggest...</td>\n",
       "      <td>Rainin Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>325.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Diamond Mountain District</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Nicely oaked blackberry, licorice, vanilla and...</td>\n",
       "      <td>6 Años Reserva Premium</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Ribera del Duero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>Valduero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>France</td>\n",
       "      <td>Coming from a seven-acre vineyard named after ...</td>\n",
       "      <td>Le Pigeonnier</td>\n",
       "      <td>95</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Cahors</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Château Lagrézette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>US</td>\n",
       "      <td>This fresh and lively medium-bodied wine is be...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Gary Farrell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>US</td>\n",
       "      <td>Heitz has made this stellar rosé from the rare...</td>\n",
       "      <td>Grignolino</td>\n",
       "      <td>95</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Rosé</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Alluring, complex and powerful aromas of grill...</td>\n",
       "      <td>Prado Enea Gran Reserva</td>\n",
       "      <td>95</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Rioja</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tempranillo Blend</td>\n",
       "      <td>Muga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Tarry blackberry and cheesy oak aromas are app...</td>\n",
       "      <td>Termanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>US</td>\n",
       "      <td>The apogee of this ambitious winery's white wi...</td>\n",
       "      <td>Giallo Solare</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Edna Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Center of Effort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>US</td>\n",
       "      <td>San Jose-based producer Adam Comartin heads 1,...</td>\n",
       "      <td>R-Bar-R Ranch</td>\n",
       "      <td>95</td>\n",
       "      <td>45.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Cruz Mountains</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Comartin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Yields were down in 2015, but intensity is up,...</td>\n",
       "      <td>Maté's Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Kumeu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Kumeu River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>US</td>\n",
       "      <td>Bergström has made a Shea designate since 2003...</td>\n",
       "      <td>Shea Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>US</td>\n",
       "      <td>Focused and dense, this intense wine captures ...</td>\n",
       "      <td>Abetina</td>\n",
       "      <td>94</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>US</td>\n",
       "      <td>Cranberry, baked rhubarb, anise and crushed sl...</td>\n",
       "      <td>Garys' Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Lucia Highlands</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Roar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>US</td>\n",
       "      <td>This standout Rocks District wine brings earth...</td>\n",
       "      <td>The Funk Estate</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Walla Walla Valley (WA)</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Syrah</td>\n",
       "      <td>Saviah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150851</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Loads of toasty oak, some mild cherry and plum...</td>\n",
       "      <td>Maxima Claret</td>\n",
       "      <td>85</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "      <td>La Playa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150852</th>\n",
       "      <td>US</td>\n",
       "      <td>Aromas of sun-dried tomatoes, coffee, cinnamon...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>21.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Dry Creek Vineyard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150861</th>\n",
       "      <td>US</td>\n",
       "      <td>From a dependable producer and a fine vintage ...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>84</td>\n",
       "      <td>16.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Clos du Bois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150869</th>\n",
       "      <td>US</td>\n",
       "      <td>A clean, well made wine displaying Cab Franc's...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Cabernet Franc</td>\n",
       "      <td>Nelson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150878</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Lots of cedar is evident on the nose and palat...</td>\n",
       "      <td>Finis Terrae</td>\n",
       "      <td>83</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Maipo Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cabernet Sauvignon-Merlot</td>\n",
       "      <td>Cousiño-Macul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150879</th>\n",
       "      <td>US</td>\n",
       "      <td>A heavy wine, atypical of the appellation, whi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>16.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Edmeades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150882</th>\n",
       "      <td>Chile</td>\n",
       "      <td>Red-berry fruit with a heavy dose of herb and ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Casablanca Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Red Blend</td>\n",
       "      <td>Primus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150889</th>\n",
       "      <td>US</td>\n",
       "      <td>A bizarre style of wine. The aromas are Port-l...</td>\n",
       "      <td>Lafond Vineyard</td>\n",
       "      <td>82</td>\n",
       "      <td>35.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Ynez Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Lafond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150906</th>\n",
       "      <td>France</td>\n",
       "      <td>This lovely wine, a Monopole, is already showi...</td>\n",
       "      <td>Clos des Reas</td>\n",
       "      <td>93</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150907</th>\n",
       "      <td>France</td>\n",
       "      <td>Rion holds back on the new oak, letting the pu...</td>\n",
       "      <td>Les Beaux-Monts</td>\n",
       "      <td>92</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150908</th>\n",
       "      <td>France</td>\n",
       "      <td>Another premier cru from Michel Gros, this one...</td>\n",
       "      <td>Aux Brulees</td>\n",
       "      <td>90</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150909</th>\n",
       "      <td>France</td>\n",
       "      <td>This is a lovely, fragrant Burgundy, with a sm...</td>\n",
       "      <td>Clos dea Argillieres</td>\n",
       "      <td>89</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Nuits-St.-Georges</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150910</th>\n",
       "      <td>France</td>\n",
       "      <td>Scents of graham cracker and malted milk choco...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chambolle-Musigny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150911</th>\n",
       "      <td>France</td>\n",
       "      <td>This needs a good bit of breathing time, then ...</td>\n",
       "      <td>Les Chaliots</td>\n",
       "      <td>87</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Nuits-St.-Georges</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150912</th>\n",
       "      <td>France</td>\n",
       "      <td>The nose is dominated by the attractive scents...</td>\n",
       "      <td>Les Charmes</td>\n",
       "      <td>87</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chambolle-Musigny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150913</th>\n",
       "      <td>France</td>\n",
       "      <td>Inky and rustic, yet in a refined manner. This...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Rhône Valley</td>\n",
       "      <td>Châteauneuf-du-Pape</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhône-style Red Blend</td>\n",
       "      <td>Le Vieux Donjon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150914</th>\n",
       "      <td>US</td>\n",
       "      <td>Old-gold in color, and thick and syrupy. The a...</td>\n",
       "      <td>Late Harvest Cluster Select</td>\n",
       "      <td>94</td>\n",
       "      <td>25.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>White Riesling</td>\n",
       "      <td>Navarro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150915</th>\n",
       "      <td>US</td>\n",
       "      <td>Decades ago, Beringer’s then-winemaker Myron N...</td>\n",
       "      <td>Nightingale</td>\n",
       "      <td>93</td>\n",
       "      <td>30.0</td>\n",
       "      <td>California</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Beringer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150916</th>\n",
       "      <td>US</td>\n",
       "      <td>An impressive wine that presents a full bouque...</td>\n",
       "      <td>J. Schram</td>\n",
       "      <td>93</td>\n",
       "      <td>65.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Schramsberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150917</th>\n",
       "      <td>France</td>\n",
       "      <td>Light and elegant, this spicy, lively wine is ...</td>\n",
       "      <td>Brut Mosaïque</td>\n",
       "      <td>92</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150918</th>\n",
       "      <td>France</td>\n",
       "      <td>Jacquart makes a full-bodied, ripe style of Ch...</td>\n",
       "      <td>Cuvée Mosaïque</td>\n",
       "      <td>92</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150919</th>\n",
       "      <td>France</td>\n",
       "      <td>This classy example opens with a very floral n...</td>\n",
       "      <td>Cuvée President</td>\n",
       "      <td>91</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>H.Germain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150920</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Rich and mature aromas of smoke, earth and her...</td>\n",
       "      <td>Brut Riserva</td>\n",
       "      <td>91</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>Trento</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Letrari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150921</th>\n",
       "      <td>France</td>\n",
       "      <td>Shows some older notes: a bouquet of toasted w...</td>\n",
       "      <td>Blanc de Blancs Brut Mosaïque</td>\n",
       "      <td>91</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150923</th>\n",
       "      <td>France</td>\n",
       "      <td>Rich and toasty, with tiny bubbles. The bouque...</td>\n",
       "      <td>Demi-Sec</td>\n",
       "      <td>91</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150924</th>\n",
       "      <td>France</td>\n",
       "      <td>Really fine for a low-acid vintage, there's an...</td>\n",
       "      <td>Diamant Bleu</td>\n",
       "      <td>91</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Heidsieck &amp; Co Monopole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150925</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Many people feel Fiano represents southern Ita...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Southern Italy</td>\n",
       "      <td>Fiano di Avellino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Feudi di San Gregorio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150926</th>\n",
       "      <td>France</td>\n",
       "      <td>Offers an intriguing nose with ginger, lime an...</td>\n",
       "      <td>Cuvée Prestige</td>\n",
       "      <td>91</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>H.Germain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150927</th>\n",
       "      <td>Italy</td>\n",
       "      <td>This classic example comes from a cru vineyard...</td>\n",
       "      <td>Terre di Dora</td>\n",
       "      <td>91</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Southern Italy</td>\n",
       "      <td>Fiano di Avellino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Terredora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150928</th>\n",
       "      <td>France</td>\n",
       "      <td>A perfect salmon shade, with scents of peaches...</td>\n",
       "      <td>Grand Brut Rosé</td>\n",
       "      <td>90</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Gosset</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103342 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            country                                        description  \\\n",
       "0                US  This tremendous 100% varietal wine hails from ...   \n",
       "1             Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2                US  Mac Watson honors the memory of a wine once ma...   \n",
       "3                US  This spent 20 months in 30% new French oak, an...   \n",
       "4            France  This is the top wine from La Bégude, named aft...   \n",
       "5             Spain  Deep, dense and pure from the opening bell, th...   \n",
       "6             Spain  Slightly gritty black-fruit aromas include a s...   \n",
       "7             Spain  Lush cedary black-fruit aromas are luxe and of...   \n",
       "8                US  This re-named vineyard was formerly bottled as...   \n",
       "9                US  The producer sources from two blocks of the vi...   \n",
       "10            Italy  Elegance, complexity and structure come togeth...   \n",
       "11               US  From 18-year-old vines, this supple well-balan...   \n",
       "12               US  A standout even in this terrific lineup of 201...   \n",
       "13           France  This wine is in peak condition. The tannins an...   \n",
       "14               US  With its sophisticated mix of mineral, acid an...   \n",
       "15               US  First made in 2006, this succulent luscious Ch...   \n",
       "16               US  This blockbuster, powerhouse of a wine suggest...   \n",
       "17            Spain  Nicely oaked blackberry, licorice, vanilla and...   \n",
       "18           France  Coming from a seven-acre vineyard named after ...   \n",
       "19               US  This fresh and lively medium-bodied wine is be...   \n",
       "20               US  Heitz has made this stellar rosé from the rare...   \n",
       "21            Spain  Alluring, complex and powerful aromas of grill...   \n",
       "22            Spain  Tarry blackberry and cheesy oak aromas are app...   \n",
       "23               US  The apogee of this ambitious winery's white wi...   \n",
       "24               US  San Jose-based producer Adam Comartin heads 1,...   \n",
       "25      New Zealand  Yields were down in 2015, but intensity is up,...   \n",
       "26               US  Bergström has made a Shea designate since 2003...   \n",
       "27               US  Focused and dense, this intense wine captures ...   \n",
       "28               US  Cranberry, baked rhubarb, anise and crushed sl...   \n",
       "29               US  This standout Rocks District wine brings earth...   \n",
       "...             ...                                                ...   \n",
       "150851        Chile  Loads of toasty oak, some mild cherry and plum...   \n",
       "150852           US  Aromas of sun-dried tomatoes, coffee, cinnamon...   \n",
       "150861           US  From a dependable producer and a fine vintage ...   \n",
       "150869           US  A clean, well made wine displaying Cab Franc's...   \n",
       "150878        Chile  Lots of cedar is evident on the nose and palat...   \n",
       "150879           US  A heavy wine, atypical of the appellation, whi...   \n",
       "150882        Chile  Red-berry fruit with a heavy dose of herb and ...   \n",
       "150889           US  A bizarre style of wine. The aromas are Port-l...   \n",
       "150906       France  This lovely wine, a Monopole, is already showi...   \n",
       "150907       France  Rion holds back on the new oak, letting the pu...   \n",
       "150908       France  Another premier cru from Michel Gros, this one...   \n",
       "150909       France  This is a lovely, fragrant Burgundy, with a sm...   \n",
       "150910       France  Scents of graham cracker and malted milk choco...   \n",
       "150911       France  This needs a good bit of breathing time, then ...   \n",
       "150912       France  The nose is dominated by the attractive scents...   \n",
       "150913       France  Inky and rustic, yet in a refined manner. This...   \n",
       "150914           US  Old-gold in color, and thick and syrupy. The a...   \n",
       "150915           US  Decades ago, Beringer’s then-winemaker Myron N...   \n",
       "150916           US  An impressive wine that presents a full bouque...   \n",
       "150917       France  Light and elegant, this spicy, lively wine is ...   \n",
       "150918       France  Jacquart makes a full-bodied, ripe style of Ch...   \n",
       "150919       France  This classy example opens with a very floral n...   \n",
       "150920        Italy  Rich and mature aromas of smoke, earth and her...   \n",
       "150921       France  Shows some older notes: a bouquet of toasted w...   \n",
       "150923       France  Rich and toasty, with tiny bubbles. The bouque...   \n",
       "150924       France  Really fine for a low-acid vintage, there's an...   \n",
       "150925        Italy  Many people feel Fiano represents southern Ita...   \n",
       "150926       France  Offers an intriguing nose with ginger, lime an...   \n",
       "150927        Italy  This classic example comes from a cru vineyard...   \n",
       "150928       France  A perfect salmon shade, with scents of peaches...   \n",
       "\n",
       "                                 designation  points  price  \\\n",
       "0                          Martha's Vineyard      96  235.0   \n",
       "1       Carodorum Selección Especial Reserva      96  110.0   \n",
       "2              Special Selected Late Harvest      96   90.0   \n",
       "3                                    Reserve      96   65.0   \n",
       "4                                 La Brûlade      95   66.0   \n",
       "5                                  Numanthia      95   73.0   \n",
       "6                                  San Román      95   65.0   \n",
       "7                    Carodorum Único Crianza      95  110.0   \n",
       "8                                     Silice      95   65.0   \n",
       "9                       Gap's Crown Vineyard      95   60.0   \n",
       "10                        Ronco della Chiesa      95   80.0   \n",
       "11           Estate Vineyard Wadensvil Block      95   48.0   \n",
       "12                            Weber Vineyard      95   48.0   \n",
       "13                   Château Montus Prestige      95   90.0   \n",
       "14                            Grace Vineyard      95  185.0   \n",
       "15                                    Sigrid      95   90.0   \n",
       "16                           Rainin Vineyard      95  325.0   \n",
       "17                    6 Años Reserva Premium      95   80.0   \n",
       "18                             Le Pigeonnier      95  290.0   \n",
       "19                      Gap's Crown Vineyard      95   75.0   \n",
       "20                                Grignolino      95   24.0   \n",
       "21                   Prado Enea Gran Reserva      95   79.0   \n",
       "22                                Termanthia      95  220.0   \n",
       "23                             Giallo Solare      95   60.0   \n",
       "24                             R-Bar-R Ranch      95   45.0   \n",
       "25                           Maté's Vineyard      94   57.0   \n",
       "26                             Shea Vineyard      94   62.0   \n",
       "27                                   Abetina      94  105.0   \n",
       "28                           Garys' Vineyard      94   60.0   \n",
       "29                           The Funk Estate      94   60.0   \n",
       "...                                      ...     ...    ...   \n",
       "150851                         Maxima Claret      85   21.0   \n",
       "150852                                   NaN      85   21.0   \n",
       "150861                               Reserve      84   16.0   \n",
       "150869                                   NaN      84   24.0   \n",
       "150878                          Finis Terrae      83   32.0   \n",
       "150879                                   NaN      83   16.0   \n",
       "150882                                   NaN      83   20.0   \n",
       "150889                       Lafond Vineyard      82   35.0   \n",
       "150906                         Clos des Reas      93   65.0   \n",
       "150907                       Les Beaux-Monts      92   52.0   \n",
       "150908                           Aux Brulees      90   65.0   \n",
       "150909                  Clos dea Argillieres      89   52.0   \n",
       "150910                                   NaN      89   38.0   \n",
       "150911                          Les Chaliots      87   37.0   \n",
       "150912                           Les Charmes      87   65.0   \n",
       "150913                                   NaN      94   30.0   \n",
       "150914           Late Harvest Cluster Select      94   25.0   \n",
       "150915                           Nightingale      93   30.0   \n",
       "150916                             J. Schram      93   65.0   \n",
       "150917                         Brut Mosaïque      92   30.0   \n",
       "150918                        Cuvée Mosaïque      92   38.0   \n",
       "150919                       Cuvée President      91   37.0   \n",
       "150920                          Brut Riserva      91   19.0   \n",
       "150921         Blanc de Blancs Brut Mosaïque      91   38.0   \n",
       "150923                              Demi-Sec      91   30.0   \n",
       "150924                          Diamant Bleu      91   70.0   \n",
       "150925                                   NaN      91   20.0   \n",
       "150926                        Cuvée Prestige      91   27.0   \n",
       "150927                         Terre di Dora      91   20.0   \n",
       "150928                       Grand Brut Rosé      90   52.0   \n",
       "\n",
       "                  province                   region_1  \\\n",
       "0               California                Napa Valley   \n",
       "1           Northern Spain                       Toro   \n",
       "2               California             Knights Valley   \n",
       "3                   Oregon          Willamette Valley   \n",
       "4                 Provence                     Bandol   \n",
       "5           Northern Spain                       Toro   \n",
       "6           Northern Spain                       Toro   \n",
       "7           Northern Spain                       Toro   \n",
       "8                   Oregon         Chehalem Mountains   \n",
       "9               California               Sonoma Coast   \n",
       "10      Northeastern Italy                     Collio   \n",
       "11                  Oregon               Ribbon Ridge   \n",
       "12                  Oregon               Dundee Hills   \n",
       "13        Southwest France                    Madiran   \n",
       "14                  Oregon               Dundee Hills   \n",
       "15                  Oregon          Willamette Valley   \n",
       "16              California  Diamond Mountain District   \n",
       "17          Northern Spain           Ribera del Duero   \n",
       "18        Southwest France                     Cahors   \n",
       "19              California               Sonoma Coast   \n",
       "20              California                Napa Valley   \n",
       "21          Northern Spain                      Rioja   \n",
       "22          Northern Spain                       Toro   \n",
       "23              California                Edna Valley   \n",
       "24              California       Santa Cruz Mountains   \n",
       "25                   Kumeu                        NaN   \n",
       "26                  Oregon          Willamette Valley   \n",
       "27                  Oregon          Willamette Valley   \n",
       "28              California      Santa Lucia Highlands   \n",
       "29              Washington    Walla Walla Valley (WA)   \n",
       "...                    ...                        ...   \n",
       "150851        Maipo Valley                        NaN   \n",
       "150852          California              Sonoma County   \n",
       "150861          California              Sonoma County   \n",
       "150869          California              Sonoma Valley   \n",
       "150878        Maipo Valley                        NaN   \n",
       "150879          California            Anderson Valley   \n",
       "150882   Casablanca Valley                        NaN   \n",
       "150889          California          Santa Ynez Valley   \n",
       "150906            Burgundy              Vosne-Romanée   \n",
       "150907            Burgundy              Vosne-Romanée   \n",
       "150908            Burgundy              Vosne-Romanée   \n",
       "150909            Burgundy          Nuits-St.-Georges   \n",
       "150910            Burgundy          Chambolle-Musigny   \n",
       "150911            Burgundy          Nuits-St.-Georges   \n",
       "150912            Burgundy          Chambolle-Musigny   \n",
       "150913        Rhône Valley        Châteauneuf-du-Pape   \n",
       "150914          California            Anderson Valley   \n",
       "150915          California                North Coast   \n",
       "150916          California                Napa Valley   \n",
       "150917           Champagne                  Champagne   \n",
       "150918           Champagne                  Champagne   \n",
       "150919           Champagne                  Champagne   \n",
       "150920  Northeastern Italy                     Trento   \n",
       "150921           Champagne                  Champagne   \n",
       "150923           Champagne                  Champagne   \n",
       "150924           Champagne                  Champagne   \n",
       "150925      Southern Italy          Fiano di Avellino   \n",
       "150926           Champagne                  Champagne   \n",
       "150927      Southern Italy          Fiano di Avellino   \n",
       "150928           Champagne                  Champagne   \n",
       "\n",
       "                       region_2                    variety  \\\n",
       "0                          Napa         Cabernet Sauvignon   \n",
       "1                           NaN              Tinta de Toro   \n",
       "2                        Sonoma            Sauvignon Blanc   \n",
       "3             Willamette Valley                 Pinot Noir   \n",
       "4                           NaN         Provence red blend   \n",
       "5                           NaN              Tinta de Toro   \n",
       "6                           NaN              Tinta de Toro   \n",
       "7                           NaN              Tinta de Toro   \n",
       "8             Willamette Valley                 Pinot Noir   \n",
       "9                        Sonoma                 Pinot Noir   \n",
       "10                          NaN                   Friulano   \n",
       "11            Willamette Valley                 Pinot Noir   \n",
       "12            Willamette Valley                 Pinot Noir   \n",
       "13                          NaN                     Tannat   \n",
       "14            Willamette Valley                 Pinot Noir   \n",
       "15            Willamette Valley                 Chardonnay   \n",
       "16                         Napa         Cabernet Sauvignon   \n",
       "17                          NaN                Tempranillo   \n",
       "18                          NaN                     Malbec   \n",
       "19                       Sonoma                 Pinot Noir   \n",
       "20                         Napa                       Rosé   \n",
       "21                          NaN          Tempranillo Blend   \n",
       "22                          NaN              Tinta de Toro   \n",
       "23                Central Coast                 Chardonnay   \n",
       "24                Central Coast                 Pinot Noir   \n",
       "25                          NaN                 Chardonnay   \n",
       "26                          NaN                 Pinot Noir   \n",
       "27            Willamette Valley                 Pinot Noir   \n",
       "28                Central Coast                 Pinot Noir   \n",
       "29              Columbia Valley                      Syrah   \n",
       "...                         ...                        ...   \n",
       "150851                      NaN   Bordeaux-style Red Blend   \n",
       "150852                   Sonoma                     Merlot   \n",
       "150861                   Sonoma                 Chardonnay   \n",
       "150869                   Sonoma             Cabernet Franc   \n",
       "150878                      NaN  Cabernet Sauvignon-Merlot   \n",
       "150879  Mendocino/Lake Counties                 Pinot Noir   \n",
       "150882                      NaN                  Red Blend   \n",
       "150889            Central Coast                 Pinot Noir   \n",
       "150906                      NaN                 Pinot Noir   \n",
       "150907                      NaN                 Pinot Noir   \n",
       "150908                      NaN                 Pinot Noir   \n",
       "150909                      NaN                 Pinot Noir   \n",
       "150910                      NaN                 Pinot Noir   \n",
       "150911                      NaN                 Pinot Noir   \n",
       "150912                      NaN                 Pinot Noir   \n",
       "150913                      NaN      Rhône-style Red Blend   \n",
       "150914  Mendocino/Lake Counties             White Riesling   \n",
       "150915              North Coast                White Blend   \n",
       "150916                     Napa            Champagne Blend   \n",
       "150917                      NaN            Champagne Blend   \n",
       "150918                      NaN            Champagne Blend   \n",
       "150919                      NaN            Champagne Blend   \n",
       "150920                      NaN            Champagne Blend   \n",
       "150921                      NaN            Champagne Blend   \n",
       "150923                      NaN            Champagne Blend   \n",
       "150924                      NaN            Champagne Blend   \n",
       "150925                      NaN                White Blend   \n",
       "150926                      NaN            Champagne Blend   \n",
       "150927                      NaN                White Blend   \n",
       "150928                      NaN            Champagne Blend   \n",
       "\n",
       "                         winery  \n",
       "0                         Heitz  \n",
       "1       Bodega Carmen Rodríguez  \n",
       "2                      Macauley  \n",
       "3                         Ponzi  \n",
       "4          Domaine de la Bégude  \n",
       "5                     Numanthia  \n",
       "6                      Maurodos  \n",
       "7       Bodega Carmen Rodríguez  \n",
       "8                     Bergström  \n",
       "9                     Blue Farm  \n",
       "10             Borgo del Tiglio  \n",
       "11       Patricia Green Cellars  \n",
       "12       Patricia Green Cellars  \n",
       "13            Vignobles Brumont  \n",
       "14               Domaine Serene  \n",
       "15                    Bergström  \n",
       "16                         Hall  \n",
       "17                     Valduero  \n",
       "18           Château Lagrézette  \n",
       "19                 Gary Farrell  \n",
       "20                        Heitz  \n",
       "21                         Muga  \n",
       "22                    Numanthia  \n",
       "23             Center of Effort  \n",
       "24                     Comartin  \n",
       "25                  Kumeu River  \n",
       "26                    Bergström  \n",
       "27                        Ponzi  \n",
       "28                         Roar  \n",
       "29                       Saviah  \n",
       "...                         ...  \n",
       "150851                 La Playa  \n",
       "150852       Dry Creek Vineyard  \n",
       "150861             Clos du Bois  \n",
       "150869                   Nelson  \n",
       "150878            Cousiño-Macul  \n",
       "150879                 Edmeades  \n",
       "150882                   Primus  \n",
       "150889                   Lafond  \n",
       "150906              Michel Gros  \n",
       "150907              Daniel Rion  \n",
       "150908              Michel Gros  \n",
       "150909              Daniel Rion  \n",
       "150910              Michel Gros  \n",
       "150911              Michel Gros  \n",
       "150912              Daniel Rion  \n",
       "150913          Le Vieux Donjon  \n",
       "150914                  Navarro  \n",
       "150915                 Beringer  \n",
       "150916              Schramsberg  \n",
       "150917                 Jacquart  \n",
       "150918                 Jacquart  \n",
       "150919                H.Germain  \n",
       "150920                  Letrari  \n",
       "150921                 Jacquart  \n",
       "150923                 Jacquart  \n",
       "150924  Heidsieck & Co Monopole  \n",
       "150925    Feudi di San Gregorio  \n",
       "150926                H.Germain  \n",
       "150927                Terredora  \n",
       "150928                   Gosset  \n",
       "\n",
       "[103342 rows x 10 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#и отбираем данные\n",
    "\n",
    "temp = data[mask]\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>US</td>\n",
       "      <td>This re-named vineyard was formerly bottled as...</td>\n",
       "      <td>Silice</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Chehalem Mountains</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US</td>\n",
       "      <td>The producer sources from two blocks of the vi...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Blue Farm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>US</td>\n",
       "      <td>From 18-year-old vines, this supple well-balan...</td>\n",
       "      <td>Estate Vineyard Wadensvil Block</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Ribbon Ridge</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>US</td>\n",
       "      <td>A standout even in this terrific lineup of 201...</td>\n",
       "      <td>Weber Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>This wine is in peak condition. The tannins an...</td>\n",
       "      <td>Château Montus Prestige</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Madiran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tannat</td>\n",
       "      <td>Vignobles Brumont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>US</td>\n",
       "      <td>With its sophisticated mix of mineral, acid an...</td>\n",
       "      <td>Grace Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Domaine Serene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US</td>\n",
       "      <td>First made in 2006, this succulent luscious Ch...</td>\n",
       "      <td>Sigrid</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>US</td>\n",
       "      <td>This blockbuster, powerhouse of a wine suggest...</td>\n",
       "      <td>Rainin Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>325.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Diamond Mountain District</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>France</td>\n",
       "      <td>Coming from a seven-acre vineyard named after ...</td>\n",
       "      <td>Le Pigeonnier</td>\n",
       "      <td>95</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Cahors</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Château Lagrézette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>US</td>\n",
       "      <td>This fresh and lively medium-bodied wine is be...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Gary Farrell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>US</td>\n",
       "      <td>Heitz has made this stellar rosé from the rare...</td>\n",
       "      <td>Grignolino</td>\n",
       "      <td>95</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Rosé</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>US</td>\n",
       "      <td>The apogee of this ambitious winery's white wi...</td>\n",
       "      <td>Giallo Solare</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Edna Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Center of Effort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>US</td>\n",
       "      <td>San Jose-based producer Adam Comartin heads 1,...</td>\n",
       "      <td>R-Bar-R Ranch</td>\n",
       "      <td>95</td>\n",
       "      <td>45.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Cruz Mountains</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Comartin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>US</td>\n",
       "      <td>Bergström has made a Shea designate since 2003...</td>\n",
       "      <td>Shea Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>US</td>\n",
       "      <td>Focused and dense, this intense wine captures ...</td>\n",
       "      <td>Abetina</td>\n",
       "      <td>94</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>US</td>\n",
       "      <td>Cranberry, baked rhubarb, anise and crushed sl...</td>\n",
       "      <td>Garys' Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Lucia Highlands</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Roar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>US</td>\n",
       "      <td>This standout Rocks District wine brings earth...</td>\n",
       "      <td>The Funk Estate</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Walla Walla Valley (WA)</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Syrah</td>\n",
       "      <td>Saviah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>US</td>\n",
       "      <td>Steely and perfumed, this wine sees only 20% n...</td>\n",
       "      <td>Babushka</td>\n",
       "      <td>90</td>\n",
       "      <td>37.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Russian River Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Zepaltas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>France</td>\n",
       "      <td>Pale in color, this is nutty in character, wit...</td>\n",
       "      <td>Nonpareil Trésor Rosé Brut</td>\n",
       "      <td>90</td>\n",
       "      <td>22.0</td>\n",
       "      <td>France Other</td>\n",
       "      <td>Vin Mousseux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sparkling Blend</td>\n",
       "      <td>Bouvet-Ladubay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>US</td>\n",
       "      <td>The aromas entice with notes of wet stone, hon...</td>\n",
       "      <td>Conner Lee Vineyard</td>\n",
       "      <td>90</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Columbia Valley (WA)</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Buty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>France</td>\n",
       "      <td>Gingery spice notes accent fresh pear and melo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Rhône Valley</td>\n",
       "      <td>Châteauneuf-du-Pape</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhône-style White Blend</td>\n",
       "      <td>Clos de L'Oratoire des Papes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>US</td>\n",
       "      <td>This is an aromatic brooder with aromas of sco...</td>\n",
       "      <td>Private Reserve</td>\n",
       "      <td>90</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petite Sirah</td>\n",
       "      <td>Huston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>France</td>\n",
       "      <td>Dark in color and in flavor profile, this medi...</td>\n",
       "      <td>Coteaux</td>\n",
       "      <td>90</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Rhône Valley</td>\n",
       "      <td>Cornas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Syrah</td>\n",
       "      <td>Tardieu-Laurent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>US</td>\n",
       "      <td>Blended with 9% Malbec, 9% Cabernet Franc and ...</td>\n",
       "      <td>Estate Grown</td>\n",
       "      <td>90</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Mount Veeder</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Brandlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>US</td>\n",
       "      <td>The aromas of blue fruit, herbs and spice are ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Red Mountain</td>\n",
       "      <td>Columbia Valley</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Canvasback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>France</td>\n",
       "      <td>This structured, complex Chardonnay is packed ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chassagne-Montrachet</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Chartron et Trébuchet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150827</th>\n",
       "      <td>US</td>\n",
       "      <td>Opens with blackberries and spices; fruity, fo...</td>\n",
       "      <td>Epoch II Millenium Cuvée</td>\n",
       "      <td>87</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Dry Creek Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Dry Creek Vineyard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150831</th>\n",
       "      <td>US</td>\n",
       "      <td>A rich, creamy wine, loaded with personality. ...</td>\n",
       "      <td>Dutton Vineyard</td>\n",
       "      <td>87</td>\n",
       "      <td>22.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Russian River Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Fritz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150832</th>\n",
       "      <td>US</td>\n",
       "      <td>Enough berry and plum richness and smoky oak t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>17.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Kenwood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150835</th>\n",
       "      <td>US</td>\n",
       "      <td>A little earthy, with plummy aromas accompanie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>28.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Arroyo Grande Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Talley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150847</th>\n",
       "      <td>US</td>\n",
       "      <td>The nose seems lean and vaguely unripe. You ge...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>20.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Newlan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150849</th>\n",
       "      <td>US</td>\n",
       "      <td>Cabernet Franc, used in Bordeaux blends and on...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>32.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Cruz Mountains</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Cabernet Franc</td>\n",
       "      <td>Clos La Chance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150852</th>\n",
       "      <td>US</td>\n",
       "      <td>Aromas of sun-dried tomatoes, coffee, cinnamon...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>21.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Merlot</td>\n",
       "      <td>Dry Creek Vineyard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150861</th>\n",
       "      <td>US</td>\n",
       "      <td>From a dependable producer and a fine vintage ...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>84</td>\n",
       "      <td>16.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Clos du Bois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150869</th>\n",
       "      <td>US</td>\n",
       "      <td>A clean, well made wine displaying Cab Franc's...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Cabernet Franc</td>\n",
       "      <td>Nelson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150879</th>\n",
       "      <td>US</td>\n",
       "      <td>A heavy wine, atypical of the appellation, whi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "      <td>16.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Edmeades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150889</th>\n",
       "      <td>US</td>\n",
       "      <td>A bizarre style of wine. The aromas are Port-l...</td>\n",
       "      <td>Lafond Vineyard</td>\n",
       "      <td>82</td>\n",
       "      <td>35.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Ynez Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Lafond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150906</th>\n",
       "      <td>France</td>\n",
       "      <td>This lovely wine, a Monopole, is already showi...</td>\n",
       "      <td>Clos des Reas</td>\n",
       "      <td>93</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150907</th>\n",
       "      <td>France</td>\n",
       "      <td>Rion holds back on the new oak, letting the pu...</td>\n",
       "      <td>Les Beaux-Monts</td>\n",
       "      <td>92</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150908</th>\n",
       "      <td>France</td>\n",
       "      <td>Another premier cru from Michel Gros, this one...</td>\n",
       "      <td>Aux Brulees</td>\n",
       "      <td>90</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Vosne-Romanée</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150909</th>\n",
       "      <td>France</td>\n",
       "      <td>This is a lovely, fragrant Burgundy, with a sm...</td>\n",
       "      <td>Clos dea Argillieres</td>\n",
       "      <td>89</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Nuits-St.-Georges</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150910</th>\n",
       "      <td>France</td>\n",
       "      <td>Scents of graham cracker and malted milk choco...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chambolle-Musigny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150911</th>\n",
       "      <td>France</td>\n",
       "      <td>This needs a good bit of breathing time, then ...</td>\n",
       "      <td>Les Chaliots</td>\n",
       "      <td>87</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Nuits-St.-Georges</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Michel Gros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150912</th>\n",
       "      <td>France</td>\n",
       "      <td>The nose is dominated by the attractive scents...</td>\n",
       "      <td>Les Charmes</td>\n",
       "      <td>87</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Burgundy</td>\n",
       "      <td>Chambolle-Musigny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Daniel Rion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150913</th>\n",
       "      <td>France</td>\n",
       "      <td>Inky and rustic, yet in a refined manner. This...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Rhône Valley</td>\n",
       "      <td>Châteauneuf-du-Pape</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhône-style Red Blend</td>\n",
       "      <td>Le Vieux Donjon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150914</th>\n",
       "      <td>US</td>\n",
       "      <td>Old-gold in color, and thick and syrupy. The a...</td>\n",
       "      <td>Late Harvest Cluster Select</td>\n",
       "      <td>94</td>\n",
       "      <td>25.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Anderson Valley</td>\n",
       "      <td>Mendocino/Lake Counties</td>\n",
       "      <td>White Riesling</td>\n",
       "      <td>Navarro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150915</th>\n",
       "      <td>US</td>\n",
       "      <td>Decades ago, Beringer’s then-winemaker Myron N...</td>\n",
       "      <td>Nightingale</td>\n",
       "      <td>93</td>\n",
       "      <td>30.0</td>\n",
       "      <td>California</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>North Coast</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Beringer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150916</th>\n",
       "      <td>US</td>\n",
       "      <td>An impressive wine that presents a full bouque...</td>\n",
       "      <td>J. Schram</td>\n",
       "      <td>93</td>\n",
       "      <td>65.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Schramsberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150917</th>\n",
       "      <td>France</td>\n",
       "      <td>Light and elegant, this spicy, lively wine is ...</td>\n",
       "      <td>Brut Mosaïque</td>\n",
       "      <td>92</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150918</th>\n",
       "      <td>France</td>\n",
       "      <td>Jacquart makes a full-bodied, ripe style of Ch...</td>\n",
       "      <td>Cuvée Mosaïque</td>\n",
       "      <td>92</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150919</th>\n",
       "      <td>France</td>\n",
       "      <td>This classy example opens with a very floral n...</td>\n",
       "      <td>Cuvée President</td>\n",
       "      <td>91</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>H.Germain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150921</th>\n",
       "      <td>France</td>\n",
       "      <td>Shows some older notes: a bouquet of toasted w...</td>\n",
       "      <td>Blanc de Blancs Brut Mosaïque</td>\n",
       "      <td>91</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150923</th>\n",
       "      <td>France</td>\n",
       "      <td>Rich and toasty, with tiny bubbles. The bouque...</td>\n",
       "      <td>Demi-Sec</td>\n",
       "      <td>91</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Jacquart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150924</th>\n",
       "      <td>France</td>\n",
       "      <td>Really fine for a low-acid vintage, there's an...</td>\n",
       "      <td>Diamant Bleu</td>\n",
       "      <td>91</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Heidsieck &amp; Co Monopole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150926</th>\n",
       "      <td>France</td>\n",
       "      <td>Offers an intriguing nose with ginger, lime an...</td>\n",
       "      <td>Cuvée Prestige</td>\n",
       "      <td>91</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>H.Germain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150928</th>\n",
       "      <td>France</td>\n",
       "      <td>A perfect salmon shade, with scents of peaches...</td>\n",
       "      <td>Grand Brut Rosé</td>\n",
       "      <td>90</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Champagne Blend</td>\n",
       "      <td>Gosset</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63803 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country                                        description  \\\n",
       "0           US  This tremendous 100% varietal wine hails from ...   \n",
       "2           US  Mac Watson honors the memory of a wine once ma...   \n",
       "3           US  This spent 20 months in 30% new French oak, an...   \n",
       "4       France  This is the top wine from La Bégude, named aft...   \n",
       "8           US  This re-named vineyard was formerly bottled as...   \n",
       "9           US  The producer sources from two blocks of the vi...   \n",
       "11          US  From 18-year-old vines, this supple well-balan...   \n",
       "12          US  A standout even in this terrific lineup of 201...   \n",
       "13      France  This wine is in peak condition. The tannins an...   \n",
       "14          US  With its sophisticated mix of mineral, acid an...   \n",
       "15          US  First made in 2006, this succulent luscious Ch...   \n",
       "16          US  This blockbuster, powerhouse of a wine suggest...   \n",
       "18      France  Coming from a seven-acre vineyard named after ...   \n",
       "19          US  This fresh and lively medium-bodied wine is be...   \n",
       "20          US  Heitz has made this stellar rosé from the rare...   \n",
       "23          US  The apogee of this ambitious winery's white wi...   \n",
       "24          US  San Jose-based producer Adam Comartin heads 1,...   \n",
       "26          US  Bergström has made a Shea designate since 2003...   \n",
       "27          US  Focused and dense, this intense wine captures ...   \n",
       "28          US  Cranberry, baked rhubarb, anise and crushed sl...   \n",
       "29          US  This standout Rocks District wine brings earth...   \n",
       "31          US  Steely and perfumed, this wine sees only 20% n...   \n",
       "33      France  Pale in color, this is nutty in character, wit...   \n",
       "34          US  The aromas entice with notes of wet stone, hon...   \n",
       "36      France  Gingery spice notes accent fresh pear and melo...   \n",
       "42          US  This is an aromatic brooder with aromas of sco...   \n",
       "44      France  Dark in color and in flavor profile, this medi...   \n",
       "47          US  Blended with 9% Malbec, 9% Cabernet Franc and ...   \n",
       "49          US  The aromas of blue fruit, herbs and spice are ...   \n",
       "51      France  This structured, complex Chardonnay is packed ...   \n",
       "...        ...                                                ...   \n",
       "150827      US  Opens with blackberries and spices; fruity, fo...   \n",
       "150831      US  A rich, creamy wine, loaded with personality. ...   \n",
       "150832      US  Enough berry and plum richness and smoky oak t...   \n",
       "150835      US  A little earthy, with plummy aromas accompanie...   \n",
       "150847      US  The nose seems lean and vaguely unripe. You ge...   \n",
       "150849      US  Cabernet Franc, used in Bordeaux blends and on...   \n",
       "150852      US  Aromas of sun-dried tomatoes, coffee, cinnamon...   \n",
       "150861      US  From a dependable producer and a fine vintage ...   \n",
       "150869      US  A clean, well made wine displaying Cab Franc's...   \n",
       "150879      US  A heavy wine, atypical of the appellation, whi...   \n",
       "150889      US  A bizarre style of wine. The aromas are Port-l...   \n",
       "150906  France  This lovely wine, a Monopole, is already showi...   \n",
       "150907  France  Rion holds back on the new oak, letting the pu...   \n",
       "150908  France  Another premier cru from Michel Gros, this one...   \n",
       "150909  France  This is a lovely, fragrant Burgundy, with a sm...   \n",
       "150910  France  Scents of graham cracker and malted milk choco...   \n",
       "150911  France  This needs a good bit of breathing time, then ...   \n",
       "150912  France  The nose is dominated by the attractive scents...   \n",
       "150913  France  Inky and rustic, yet in a refined manner. This...   \n",
       "150914      US  Old-gold in color, and thick and syrupy. The a...   \n",
       "150915      US  Decades ago, Beringer’s then-winemaker Myron N...   \n",
       "150916      US  An impressive wine that presents a full bouque...   \n",
       "150917  France  Light and elegant, this spicy, lively wine is ...   \n",
       "150918  France  Jacquart makes a full-bodied, ripe style of Ch...   \n",
       "150919  France  This classy example opens with a very floral n...   \n",
       "150921  France  Shows some older notes: a bouquet of toasted w...   \n",
       "150923  France  Rich and toasty, with tiny bubbles. The bouque...   \n",
       "150924  France  Really fine for a low-acid vintage, there's an...   \n",
       "150926  France  Offers an intriguing nose with ginger, lime an...   \n",
       "150928  France  A perfect salmon shade, with scents of peaches...   \n",
       "\n",
       "                            designation  points  price          province  \\\n",
       "0                     Martha's Vineyard      96  235.0        California   \n",
       "2         Special Selected Late Harvest      96   90.0        California   \n",
       "3                               Reserve      96   65.0            Oregon   \n",
       "4                            La Brûlade      95   66.0          Provence   \n",
       "8                                Silice      95   65.0            Oregon   \n",
       "9                  Gap's Crown Vineyard      95   60.0        California   \n",
       "11      Estate Vineyard Wadensvil Block      95   48.0            Oregon   \n",
       "12                       Weber Vineyard      95   48.0            Oregon   \n",
       "13              Château Montus Prestige      95   90.0  Southwest France   \n",
       "14                       Grace Vineyard      95  185.0            Oregon   \n",
       "15                               Sigrid      95   90.0            Oregon   \n",
       "16                      Rainin Vineyard      95  325.0        California   \n",
       "18                        Le Pigeonnier      95  290.0  Southwest France   \n",
       "19                 Gap's Crown Vineyard      95   75.0        California   \n",
       "20                           Grignolino      95   24.0        California   \n",
       "23                        Giallo Solare      95   60.0        California   \n",
       "24                        R-Bar-R Ranch      95   45.0        California   \n",
       "26                        Shea Vineyard      94   62.0            Oregon   \n",
       "27                              Abetina      94  105.0            Oregon   \n",
       "28                      Garys' Vineyard      94   60.0        California   \n",
       "29                      The Funk Estate      94   60.0        Washington   \n",
       "31                             Babushka      90   37.0        California   \n",
       "33           Nonpareil Trésor Rosé Brut      90   22.0      France Other   \n",
       "34                  Conner Lee Vineyard      90   42.0        Washington   \n",
       "36                                  NaN      90   60.0      Rhône Valley   \n",
       "42                      Private Reserve      90   55.0             Idaho   \n",
       "44                              Coteaux      90   69.0      Rhône Valley   \n",
       "47                         Estate Grown      90   60.0        California   \n",
       "49                                  NaN      90   40.0        Washington   \n",
       "51                                  NaN      90   68.0          Burgundy   \n",
       "...                                 ...     ...    ...               ...   \n",
       "150827         Epoch II Millenium Cuvée      87   60.0        California   \n",
       "150831                  Dutton Vineyard      87   22.0        California   \n",
       "150832                              NaN      87   17.0        California   \n",
       "150835                              NaN      86   28.0        California   \n",
       "150847                              NaN      85   20.0        California   \n",
       "150849                              NaN      85   32.0        California   \n",
       "150852                              NaN      85   21.0        California   \n",
       "150861                          Reserve      84   16.0        California   \n",
       "150869                              NaN      84   24.0        California   \n",
       "150879                              NaN      83   16.0        California   \n",
       "150889                  Lafond Vineyard      82   35.0        California   \n",
       "150906                    Clos des Reas      93   65.0          Burgundy   \n",
       "150907                  Les Beaux-Monts      92   52.0          Burgundy   \n",
       "150908                      Aux Brulees      90   65.0          Burgundy   \n",
       "150909             Clos dea Argillieres      89   52.0          Burgundy   \n",
       "150910                              NaN      89   38.0          Burgundy   \n",
       "150911                     Les Chaliots      87   37.0          Burgundy   \n",
       "150912                      Les Charmes      87   65.0          Burgundy   \n",
       "150913                              NaN      94   30.0      Rhône Valley   \n",
       "150914      Late Harvest Cluster Select      94   25.0        California   \n",
       "150915                      Nightingale      93   30.0        California   \n",
       "150916                        J. Schram      93   65.0        California   \n",
       "150917                    Brut Mosaïque      92   30.0         Champagne   \n",
       "150918                   Cuvée Mosaïque      92   38.0         Champagne   \n",
       "150919                  Cuvée President      91   37.0         Champagne   \n",
       "150921    Blanc de Blancs Brut Mosaïque      91   38.0         Champagne   \n",
       "150923                         Demi-Sec      91   30.0         Champagne   \n",
       "150924                     Diamant Bleu      91   70.0         Champagne   \n",
       "150926                   Cuvée Prestige      91   27.0         Champagne   \n",
       "150928                  Grand Brut Rosé      90   52.0         Champagne   \n",
       "\n",
       "                         region_1                 region_2  \\\n",
       "0                     Napa Valley                     Napa   \n",
       "2                  Knights Valley                   Sonoma   \n",
       "3               Willamette Valley        Willamette Valley   \n",
       "4                          Bandol                      NaN   \n",
       "8              Chehalem Mountains        Willamette Valley   \n",
       "9                    Sonoma Coast                   Sonoma   \n",
       "11                   Ribbon Ridge        Willamette Valley   \n",
       "12                   Dundee Hills        Willamette Valley   \n",
       "13                        Madiran                      NaN   \n",
       "14                   Dundee Hills        Willamette Valley   \n",
       "15              Willamette Valley        Willamette Valley   \n",
       "16      Diamond Mountain District                     Napa   \n",
       "18                         Cahors                      NaN   \n",
       "19                   Sonoma Coast                   Sonoma   \n",
       "20                    Napa Valley                     Napa   \n",
       "23                    Edna Valley            Central Coast   \n",
       "24           Santa Cruz Mountains            Central Coast   \n",
       "26              Willamette Valley                      NaN   \n",
       "27              Willamette Valley        Willamette Valley   \n",
       "28          Santa Lucia Highlands            Central Coast   \n",
       "29        Walla Walla Valley (WA)          Columbia Valley   \n",
       "31           Russian River Valley                   Sonoma   \n",
       "33                   Vin Mousseux                      NaN   \n",
       "34           Columbia Valley (WA)          Columbia Valley   \n",
       "36            Châteauneuf-du-Pape                      NaN   \n",
       "42                            NaN                      NaN   \n",
       "44                         Cornas                      NaN   \n",
       "47                   Mount Veeder                     Napa   \n",
       "49                   Red Mountain          Columbia Valley   \n",
       "51           Chassagne-Montrachet                      NaN   \n",
       "...                           ...                      ...   \n",
       "150827           Dry Creek Valley                   Sonoma   \n",
       "150831       Russian River Valley                   Sonoma   \n",
       "150832              Sonoma County                   Sonoma   \n",
       "150835       Arroyo Grande Valley            Central Coast   \n",
       "150847                Napa Valley                     Napa   \n",
       "150849       Santa Cruz Mountains            Central Coast   \n",
       "150852              Sonoma County                   Sonoma   \n",
       "150861              Sonoma County                   Sonoma   \n",
       "150869              Sonoma Valley                   Sonoma   \n",
       "150879            Anderson Valley  Mendocino/Lake Counties   \n",
       "150889          Santa Ynez Valley            Central Coast   \n",
       "150906              Vosne-Romanée                      NaN   \n",
       "150907              Vosne-Romanée                      NaN   \n",
       "150908              Vosne-Romanée                      NaN   \n",
       "150909          Nuits-St.-Georges                      NaN   \n",
       "150910          Chambolle-Musigny                      NaN   \n",
       "150911          Nuits-St.-Georges                      NaN   \n",
       "150912          Chambolle-Musigny                      NaN   \n",
       "150913        Châteauneuf-du-Pape                      NaN   \n",
       "150914            Anderson Valley  Mendocino/Lake Counties   \n",
       "150915                North Coast              North Coast   \n",
       "150916                Napa Valley                     Napa   \n",
       "150917                  Champagne                      NaN   \n",
       "150918                  Champagne                      NaN   \n",
       "150919                  Champagne                      NaN   \n",
       "150921                  Champagne                      NaN   \n",
       "150923                  Champagne                      NaN   \n",
       "150924                  Champagne                      NaN   \n",
       "150926                  Champagne                      NaN   \n",
       "150928                  Champagne                      NaN   \n",
       "\n",
       "                        variety                        winery  \n",
       "0            Cabernet Sauvignon                         Heitz  \n",
       "2               Sauvignon Blanc                      Macauley  \n",
       "3                    Pinot Noir                         Ponzi  \n",
       "4            Provence red blend          Domaine de la Bégude  \n",
       "8                    Pinot Noir                     Bergström  \n",
       "9                    Pinot Noir                     Blue Farm  \n",
       "11                   Pinot Noir        Patricia Green Cellars  \n",
       "12                   Pinot Noir        Patricia Green Cellars  \n",
       "13                       Tannat             Vignobles Brumont  \n",
       "14                   Pinot Noir                Domaine Serene  \n",
       "15                   Chardonnay                     Bergström  \n",
       "16           Cabernet Sauvignon                          Hall  \n",
       "18                       Malbec            Château Lagrézette  \n",
       "19                   Pinot Noir                  Gary Farrell  \n",
       "20                         Rosé                         Heitz  \n",
       "23                   Chardonnay              Center of Effort  \n",
       "24                   Pinot Noir                      Comartin  \n",
       "26                   Pinot Noir                     Bergström  \n",
       "27                   Pinot Noir                         Ponzi  \n",
       "28                   Pinot Noir                          Roar  \n",
       "29                        Syrah                        Saviah  \n",
       "31                   Chardonnay                      Zepaltas  \n",
       "33              Sparkling Blend                Bouvet-Ladubay  \n",
       "34                   Chardonnay                          Buty  \n",
       "36      Rhône-style White Blend  Clos de L'Oratoire des Papes  \n",
       "42                 Petite Sirah                        Huston  \n",
       "44                        Syrah               Tardieu-Laurent  \n",
       "47           Cabernet Sauvignon                      Brandlin  \n",
       "49           Cabernet Sauvignon                    Canvasback  \n",
       "51                   Chardonnay         Chartron et Trébuchet  \n",
       "...                         ...                           ...  \n",
       "150827       Cabernet Sauvignon            Dry Creek Vineyard  \n",
       "150831               Chardonnay                         Fritz  \n",
       "150832                   Merlot                       Kenwood  \n",
       "150835               Pinot Noir                        Talley  \n",
       "150847                   Merlot                        Newlan  \n",
       "150849           Cabernet Franc                Clos La Chance  \n",
       "150852                   Merlot            Dry Creek Vineyard  \n",
       "150861               Chardonnay                  Clos du Bois  \n",
       "150869           Cabernet Franc                        Nelson  \n",
       "150879               Pinot Noir                      Edmeades  \n",
       "150889               Pinot Noir                        Lafond  \n",
       "150906               Pinot Noir                   Michel Gros  \n",
       "150907               Pinot Noir                   Daniel Rion  \n",
       "150908               Pinot Noir                   Michel Gros  \n",
       "150909               Pinot Noir                   Daniel Rion  \n",
       "150910               Pinot Noir                   Michel Gros  \n",
       "150911               Pinot Noir                   Michel Gros  \n",
       "150912               Pinot Noir                   Daniel Rion  \n",
       "150913    Rhône-style Red Blend               Le Vieux Donjon  \n",
       "150914           White Riesling                       Navarro  \n",
       "150915              White Blend                      Beringer  \n",
       "150916          Champagne Blend                   Schramsberg  \n",
       "150917          Champagne Blend                      Jacquart  \n",
       "150918          Champagne Blend                      Jacquart  \n",
       "150919          Champagne Blend                     H.Germain  \n",
       "150921          Champagne Blend                      Jacquart  \n",
       "150923          Champagne Blend                      Jacquart  \n",
       "150924          Champagne Blend       Heidsieck & Co Monopole  \n",
       "150926          Champagne Blend                     H.Germain  \n",
       "150928          Champagne Blend                        Gosset  \n",
       "\n",
       "[63803 rows x 10 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.price > 15) & ((data.country == 'US') | (data.country == 'France'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Мультииндексация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <th>Mirditë</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Argentina</th>\n",
       "      <th>Mendoza Province</th>\n",
       "      <td>4742</td>\n",
       "      <td>3278</td>\n",
       "      <td>4742</td>\n",
       "      <td>4706</td>\n",
       "      <td>4738</td>\n",
       "      <td>0</td>\n",
       "      <td>4742</td>\n",
       "      <td>4742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>889</td>\n",
       "      <td>658</td>\n",
       "      <td>889</td>\n",
       "      <td>881</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>889</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Australia</th>\n",
       "      <th>Australia Other</th>\n",
       "      <td>553</td>\n",
       "      <td>302</td>\n",
       "      <td>553</td>\n",
       "      <td>551</td>\n",
       "      <td>553</td>\n",
       "      <td>0</td>\n",
       "      <td>553</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New South Wales</th>\n",
       "      <td>246</td>\n",
       "      <td>144</td>\n",
       "      <td>246</td>\n",
       "      <td>244</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            description  designation  points  price  region_1  \\\n",
       "country   province                                                              \n",
       "Albania   Mirditë                     2            0       2      2         0   \n",
       "Argentina Mendoza Province         4742         3278    4742   4706      4738   \n",
       "          Other                     889          658     889    881       889   \n",
       "Australia Australia Other           553          302     553    551       553   \n",
       "          New South Wales           246          144     246    244       246   \n",
       "\n",
       "                            region_2  variety  winery  \n",
       "country   province                                     \n",
       "Albania   Mirditë                  0        2       2  \n",
       "Argentina Mendoza Province         0     4742    4742  \n",
       "          Other                    0      889     889  \n",
       "Australia Australia Other          0      553     553  \n",
       "          New South Wales          0      246     246  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ = data.groupby(['country', 'province']).count()\n",
    "data_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>province</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>44508</td>\n",
       "      <td>44508</td>\n",
       "      <td>28805</td>\n",
       "      <td>44508</td>\n",
       "      <td>44356</td>\n",
       "      <td>44508</td>\n",
       "      <td>44271</td>\n",
       "      <td>44508</td>\n",
       "      <td>44508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Unnamed: 0  description  designation  points  price  region_1  \\\n",
       "province                                                                     \n",
       "America              27           27           22      27     27         0   \n",
       "Arizona              39           39           28      39     38        39   \n",
       "California        44508        44508        28805   44508  44356     44508   \n",
       "Colorado             30           30            8      30     30        30   \n",
       "Connecticut           2            2            2       2      2         2   \n",
       "\n",
       "             region_2  variety  winery  \n",
       "province                                \n",
       "America             0       27      27  \n",
       "Arizona             0       39      39  \n",
       "California      44271    44508   44508  \n",
       "Colorado            0       30      30  \n",
       "Connecticut         0        2       2  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_.loc['US'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     44508\n",
       "description    44508\n",
       "designation    28805\n",
       "points         44508\n",
       "price          44356\n",
       "region_1       44508\n",
       "region_2       44271\n",
       "variety        44508\n",
       "winery         44508\n",
       "Name: (US, California), dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_.loc['US', 'California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Как изменять значения в табличке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>US</td>\n",
       "      <td>kotiki</td>\n",
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       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
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       "      <td>Northern Spain</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
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       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
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       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US                                             kotiki   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0,1] = 'kotiki'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>kotiki</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country                                        description  \\\n",
       "0      US                                             kotiki   \n",
       "1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1 region_2             variety                   winery  \n",
       "0        Napa Valley      kot  Cabernet Sauvignon                    Heitz  \n",
       "1               Toro      NaN       Tinta de Toro  Bodega Carmen Rodríguez  \n",
       "2     Knights Valley      kot     Sauvignon Blanc                 Macauley  \n",
       "3  Willamette Valley      kot          Pinot Noir                    Ponzi  \n",
       "4             Bandol      NaN  Provence red blend     Domaine de la Bégude  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.country == 'US', 'region_2'] = 'kot'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конкатенация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 7, 234, 23, 0, 1, 2, 3, 4, 5, 6, 7, 234, 23]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [0, 1, 2, 3]\n",
    "b = [4, 5, 6, 7, 234, 23]\n",
    "b + a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = data[0:15]\n",
    "data_2 = data[15:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
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       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US</td>\n",
       "      <td>First made in 2006, this succulent luscious Ch...</td>\n",
       "      <td>Sigrid</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>US</td>\n",
       "      <td>This blockbuster, powerhouse of a wine suggest...</td>\n",
       "      <td>Rainin Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>325.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Diamond Mountain District</td>\n",
       "      <td>kot</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Nicely oaked blackberry, licorice, vanilla and...</td>\n",
       "      <td>6 Años Reserva Premium</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Ribera del Duero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>Valduero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>France</td>\n",
       "      <td>Coming from a seven-acre vineyard named after ...</td>\n",
       "      <td>Le Pigeonnier</td>\n",
       "      <td>95</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Cahors</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Château Lagrézette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>US</td>\n",
       "      <td>This fresh and lively medium-bodied wine is be...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Gary Farrell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country                                        description  \\\n",
       "15      US  First made in 2006, this succulent luscious Ch...   \n",
       "16      US  This blockbuster, powerhouse of a wine suggest...   \n",
       "17   Spain  Nicely oaked blackberry, licorice, vanilla and...   \n",
       "18  France  Coming from a seven-acre vineyard named after ...   \n",
       "19      US  This fresh and lively medium-bodied wine is be...   \n",
       "\n",
       "               designation  points  price          province  \\\n",
       "15                  Sigrid      95   90.0            Oregon   \n",
       "16         Rainin Vineyard      95  325.0        California   \n",
       "17  6 Años Reserva Premium      95   80.0    Northern Spain   \n",
       "18           Le Pigeonnier      95  290.0  Southwest France   \n",
       "19    Gap's Crown Vineyard      95   75.0        California   \n",
       "\n",
       "                     region_1 region_2             variety              winery  \n",
       "15          Willamette Valley      kot          Chardonnay           Bergström  \n",
       "16  Diamond Mountain District      kot  Cabernet Sauvignon                Hall  \n",
       "17           Ribera del Duero      NaN         Tempranillo            Valduero  \n",
       "18                     Cahors      NaN              Malbec  Château Lagrézette  \n",
       "19               Sonoma Coast      kot          Pinot Noir        Gary Farrell  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>kotiki</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Deep, dense and pure from the opening bell, th...</td>\n",
       "      <td>Numanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Slightly gritty black-fruit aromas include a s...</td>\n",
       "      <td>San Román</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Maurodos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Lush cedary black-fruit aromas are luxe and of...</td>\n",
       "      <td>Carodorum Único Crianza</td>\n",
       "      <td>95</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>US</td>\n",
       "      <td>This re-named vineyard was formerly bottled as...</td>\n",
       "      <td>Silice</td>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Chehalem Mountains</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US</td>\n",
       "      <td>The producer sources from two blocks of the vi...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Blue Farm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Elegance, complexity and structure come togeth...</td>\n",
       "      <td>Ronco della Chiesa</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northeastern Italy</td>\n",
       "      <td>Collio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friulano</td>\n",
       "      <td>Borgo del Tiglio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>US</td>\n",
       "      <td>From 18-year-old vines, this supple well-balan...</td>\n",
       "      <td>Estate Vineyard Wadensvil Block</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Ribbon Ridge</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>US</td>\n",
       "      <td>A standout even in this terrific lineup of 201...</td>\n",
       "      <td>Weber Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Patricia Green Cellars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>This wine is in peak condition. The tannins an...</td>\n",
       "      <td>Château Montus Prestige</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Madiran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tannat</td>\n",
       "      <td>Vignobles Brumont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>US</td>\n",
       "      <td>With its sophisticated mix of mineral, acid an...</td>\n",
       "      <td>Grace Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Dundee Hills</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Domaine Serene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>US</td>\n",
       "      <td>First made in 2006, this succulent luscious Ch...</td>\n",
       "      <td>Sigrid</td>\n",
       "      <td>95</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>US</td>\n",
       "      <td>This blockbuster, powerhouse of a wine suggest...</td>\n",
       "      <td>Rainin Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>325.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Diamond Mountain District</td>\n",
       "      <td>kot</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Nicely oaked blackberry, licorice, vanilla and...</td>\n",
       "      <td>6 Años Reserva Premium</td>\n",
       "      <td>95</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Ribera del Duero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>Valduero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>France</td>\n",
       "      <td>Coming from a seven-acre vineyard named after ...</td>\n",
       "      <td>Le Pigeonnier</td>\n",
       "      <td>95</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Southwest France</td>\n",
       "      <td>Cahors</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>Château Lagrézette</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>US</td>\n",
       "      <td>This fresh and lively medium-bodied wine is be...</td>\n",
       "      <td>Gap's Crown Vineyard</td>\n",
       "      <td>95</td>\n",
       "      <td>75.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma Coast</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Gary Farrell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>US</td>\n",
       "      <td>Heitz has made this stellar rosé from the rare...</td>\n",
       "      <td>Grignolino</td>\n",
       "      <td>95</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Rosé</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Alluring, complex and powerful aromas of grill...</td>\n",
       "      <td>Prado Enea Gran Reserva</td>\n",
       "      <td>95</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Rioja</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tempranillo Blend</td>\n",
       "      <td>Muga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Tarry blackberry and cheesy oak aromas are app...</td>\n",
       "      <td>Termanthia</td>\n",
       "      <td>95</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Numanthia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>US</td>\n",
       "      <td>The apogee of this ambitious winery's white wi...</td>\n",
       "      <td>Giallo Solare</td>\n",
       "      <td>95</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Edna Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Center of Effort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>US</td>\n",
       "      <td>San Jose-based producer Adam Comartin heads 1,...</td>\n",
       "      <td>R-Bar-R Ranch</td>\n",
       "      <td>95</td>\n",
       "      <td>45.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Cruz Mountains</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Comartin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Yields were down in 2015, but intensity is up,...</td>\n",
       "      <td>Maté's Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>57.0</td>\n",
       "      <td>Kumeu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Kumeu River</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>US</td>\n",
       "      <td>Bergström has made a Shea designate since 2003...</td>\n",
       "      <td>Shea Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Bergström</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>US</td>\n",
       "      <td>Focused and dense, this intense wine captures ...</td>\n",
       "      <td>Abetina</td>\n",
       "      <td>94</td>\n",
       "      <td>105.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>US</td>\n",
       "      <td>Cranberry, baked rhubarb, anise and crushed sl...</td>\n",
       "      <td>Garys' Vineyard</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Lucia Highlands</td>\n",
       "      <td>kot</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Roar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>US</td>\n",
       "      <td>This standout Rocks District wine brings earth...</td>\n",
       "      <td>The Funk Estate</td>\n",
       "      <td>94</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Walla Walla Valley (WA)</td>\n",
       "      <td>kot</td>\n",
       "      <td>Syrah</td>\n",
       "      <td>Saviah</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country                                        description  \\\n",
       "0            US                                             kotiki   \n",
       "1         Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2            US  Mac Watson honors the memory of a wine once ma...   \n",
       "3            US  This spent 20 months in 30% new French oak, an...   \n",
       "4        France  This is the top wine from La Bégude, named aft...   \n",
       "5         Spain  Deep, dense and pure from the opening bell, th...   \n",
       "6         Spain  Slightly gritty black-fruit aromas include a s...   \n",
       "7         Spain  Lush cedary black-fruit aromas are luxe and of...   \n",
       "8            US  This re-named vineyard was formerly bottled as...   \n",
       "9            US  The producer sources from two blocks of the vi...   \n",
       "10        Italy  Elegance, complexity and structure come togeth...   \n",
       "11           US  From 18-year-old vines, this supple well-balan...   \n",
       "12           US  A standout even in this terrific lineup of 201...   \n",
       "13       France  This wine is in peak condition. The tannins an...   \n",
       "14           US  With its sophisticated mix of mineral, acid an...   \n",
       "15           US  First made in 2006, this succulent luscious Ch...   \n",
       "16           US  This blockbuster, powerhouse of a wine suggest...   \n",
       "17        Spain  Nicely oaked blackberry, licorice, vanilla and...   \n",
       "18       France  Coming from a seven-acre vineyard named after ...   \n",
       "19           US  This fresh and lively medium-bodied wine is be...   \n",
       "20           US  Heitz has made this stellar rosé from the rare...   \n",
       "21        Spain  Alluring, complex and powerful aromas of grill...   \n",
       "22        Spain  Tarry blackberry and cheesy oak aromas are app...   \n",
       "23           US  The apogee of this ambitious winery's white wi...   \n",
       "24           US  San Jose-based producer Adam Comartin heads 1,...   \n",
       "25  New Zealand  Yields were down in 2015, but intensity is up,...   \n",
       "26           US  Bergström has made a Shea designate since 2003...   \n",
       "27           US  Focused and dense, this intense wine captures ...   \n",
       "28           US  Cranberry, baked rhubarb, anise and crushed sl...   \n",
       "29           US  This standout Rocks District wine brings earth...   \n",
       "\n",
       "                             designation  points  price            province  \\\n",
       "0                      Martha's Vineyard      96  235.0          California   \n",
       "1   Carodorum Selección Especial Reserva      96  110.0      Northern Spain   \n",
       "2          Special Selected Late Harvest      96   90.0          California   \n",
       "3                                Reserve      96   65.0              Oregon   \n",
       "4                             La Brûlade      95   66.0            Provence   \n",
       "5                              Numanthia      95   73.0      Northern Spain   \n",
       "6                              San Román      95   65.0      Northern Spain   \n",
       "7                Carodorum Único Crianza      95  110.0      Northern Spain   \n",
       "8                                 Silice      95   65.0              Oregon   \n",
       "9                   Gap's Crown Vineyard      95   60.0          California   \n",
       "10                    Ronco della Chiesa      95   80.0  Northeastern Italy   \n",
       "11       Estate Vineyard Wadensvil Block      95   48.0              Oregon   \n",
       "12                        Weber Vineyard      95   48.0              Oregon   \n",
       "13               Château Montus Prestige      95   90.0    Southwest France   \n",
       "14                        Grace Vineyard      95  185.0              Oregon   \n",
       "15                                Sigrid      95   90.0              Oregon   \n",
       "16                       Rainin Vineyard      95  325.0          California   \n",
       "17                6 Años Reserva Premium      95   80.0      Northern Spain   \n",
       "18                         Le Pigeonnier      95  290.0    Southwest France   \n",
       "19                  Gap's Crown Vineyard      95   75.0          California   \n",
       "20                            Grignolino      95   24.0          California   \n",
       "21               Prado Enea Gran Reserva      95   79.0      Northern Spain   \n",
       "22                            Termanthia      95  220.0      Northern Spain   \n",
       "23                         Giallo Solare      95   60.0          California   \n",
       "24                         R-Bar-R Ranch      95   45.0          California   \n",
       "25                       Maté's Vineyard      94   57.0               Kumeu   \n",
       "26                         Shea Vineyard      94   62.0              Oregon   \n",
       "27                               Abetina      94  105.0              Oregon   \n",
       "28                       Garys' Vineyard      94   60.0          California   \n",
       "29                       The Funk Estate      94   60.0          Washington   \n",
       "\n",
       "                     region_1 region_2             variety  \\\n",
       "0                 Napa Valley      kot  Cabernet Sauvignon   \n",
       "1                        Toro      NaN       Tinta de Toro   \n",
       "2              Knights Valley      kot     Sauvignon Blanc   \n",
       "3           Willamette Valley      kot          Pinot Noir   \n",
       "4                      Bandol      NaN  Provence red blend   \n",
       "5                        Toro      NaN       Tinta de Toro   \n",
       "6                        Toro      NaN       Tinta de Toro   \n",
       "7                        Toro      NaN       Tinta de Toro   \n",
       "8          Chehalem Mountains      kot          Pinot Noir   \n",
       "9                Sonoma Coast      kot          Pinot Noir   \n",
       "10                     Collio      NaN            Friulano   \n",
       "11               Ribbon Ridge      kot          Pinot Noir   \n",
       "12               Dundee Hills      kot          Pinot Noir   \n",
       "13                    Madiran      NaN              Tannat   \n",
       "14               Dundee Hills      kot          Pinot Noir   \n",
       "15          Willamette Valley      kot          Chardonnay   \n",
       "16  Diamond Mountain District      kot  Cabernet Sauvignon   \n",
       "17           Ribera del Duero      NaN         Tempranillo   \n",
       "18                     Cahors      NaN              Malbec   \n",
       "19               Sonoma Coast      kot          Pinot Noir   \n",
       "20                Napa Valley      kot                Rosé   \n",
       "21                      Rioja      NaN   Tempranillo Blend   \n",
       "22                       Toro      NaN       Tinta de Toro   \n",
       "23                Edna Valley      kot          Chardonnay   \n",
       "24       Santa Cruz Mountains      kot          Pinot Noir   \n",
       "25                        NaN      NaN          Chardonnay   \n",
       "26          Willamette Valley      kot          Pinot Noir   \n",
       "27          Willamette Valley      kot          Pinot Noir   \n",
       "28      Santa Lucia Highlands      kot          Pinot Noir   \n",
       "29    Walla Walla Valley (WA)      kot               Syrah   \n",
       "\n",
       "                     winery  \n",
       "0                     Heitz  \n",
       "1   Bodega Carmen Rodríguez  \n",
       "2                  Macauley  \n",
       "3                     Ponzi  \n",
       "4      Domaine de la Bégude  \n",
       "5                 Numanthia  \n",
       "6                  Maurodos  \n",
       "7   Bodega Carmen Rodríguez  \n",
       "8                 Bergström  \n",
       "9                 Blue Farm  \n",
       "10         Borgo del Tiglio  \n",
       "11   Patricia Green Cellars  \n",
       "12   Patricia Green Cellars  \n",
       "13        Vignobles Brumont  \n",
       "14           Domaine Serene  \n",
       "15                Bergström  \n",
       "16                     Hall  \n",
       "17                 Valduero  \n",
       "18       Château Lagrézette  \n",
       "19             Gary Farrell  \n",
       "20                    Heitz  \n",
       "21                     Muga  \n",
       "22                Numanthia  \n",
       "23         Center of Effort  \n",
       "24                 Comartin  \n",
       "25              Kumeu River  \n",
       "26                Bergström  \n",
       "27                    Ponzi  \n",
       "28                     Roar  \n",
       "29                   Saviah  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([data_1, data_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>This tremendous 100% varietal wine hails from ...</td>\n",
       "      <td>Martha's Vineyard</td>\n",
       "      <td>96</td>\n",
       "      <td>235.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Heitz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Ripe aromas of fig, blackberry and cassis are ...</td>\n",
       "      <td>Carodorum Selección Especial Reserva</td>\n",
       "      <td>96</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Toro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tinta de Toro</td>\n",
       "      <td>Bodega Carmen Rodríguez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>Mac Watson honors the memory of a wine once ma...</td>\n",
       "      <td>Special Selected Late Harvest</td>\n",
       "      <td>96</td>\n",
       "      <td>90.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Knights Valley</td>\n",
       "      <td>Sonoma</td>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "      <td>Macauley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>This spent 20 months in 30% new French oak, an...</td>\n",
       "      <td>Reserve</td>\n",
       "      <td>96</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Ponzi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>This is the top wine from La Bégude, named aft...</td>\n",
       "      <td>La Brûlade</td>\n",
       "      <td>95</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Provence</td>\n",
       "      <td>Bandol</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Provence red blend</td>\n",
       "      <td>Domaine de la Bégude</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 country                                        description  \\\n",
       "0           0      US  This tremendous 100% varietal wine hails from ...   \n",
       "1           1   Spain  Ripe aromas of fig, blackberry and cassis are ...   \n",
       "2           2      US  Mac Watson honors the memory of a wine once ma...   \n",
       "3           3      US  This spent 20 months in 30% new French oak, an...   \n",
       "4           4  France  This is the top wine from La Bégude, named aft...   \n",
       "\n",
       "                            designation  points  price        province  \\\n",
       "0                     Martha's Vineyard      96  235.0      California   \n",
       "1  Carodorum Selección Especial Reserva      96  110.0  Northern Spain   \n",
       "2         Special Selected Late Harvest      96   90.0      California   \n",
       "3                               Reserve      96   65.0          Oregon   \n",
       "4                            La Brûlade      95   66.0        Provence   \n",
       "\n",
       "            region_1           region_2             variety  \\\n",
       "0        Napa Valley               Napa  Cabernet Sauvignon   \n",
       "1               Toro                NaN       Tinta de Toro   \n",
       "2     Knights Valley             Sonoma     Sauvignon Blanc   \n",
       "3  Willamette Valley  Willamette Valley          Pinot Noir   \n",
       "4             Bandol                NaN  Provence red blend   \n",
       "\n",
       "                    winery  \n",
       "0                    Heitz  \n",
       "1  Bodega Carmen Rodríguez  \n",
       "2                 Macauley  \n",
       "3                    Ponzi  \n",
       "4     Domaine de la Bégude  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сводные таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>85.996093</td>\n",
       "      <td>20.794881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>87.892475</td>\n",
       "      <td>31.258480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>89.276742</td>\n",
       "      <td>31.192106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>84.750000</td>\n",
       "      <td>12.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>83.240000</td>\n",
       "      <td>19.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>85.467532</td>\n",
       "      <td>11.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>88.239796</td>\n",
       "      <td>34.628866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>86.296768</td>\n",
       "      <td>19.344780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>20.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>86.280899</td>\n",
       "      <td>23.108434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>85.870968</td>\n",
       "      <td>15.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>85.833333</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>83.666667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>92.888889</td>\n",
       "      <td>47.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>88.925870</td>\n",
       "      <td>45.619885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>85.511628</td>\n",
       "      <td>18.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>88.626427</td>\n",
       "      <td>39.011078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>86.117647</td>\n",
       "      <td>21.747706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>87.329004</td>\n",
       "      <td>44.204348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>87.625000</td>\n",
       "      <td>13.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>87.176190</td>\n",
       "      <td>31.304918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>88.413664</td>\n",
       "      <td>37.547913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>85.702703</td>\n",
       "      <td>25.432432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>40.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macedonia</th>\n",
       "      <td>84.812500</td>\n",
       "      <td>15.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>84.761905</td>\n",
       "      <td>29.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moldova</th>\n",
       "      <td>84.718310</td>\n",
       "      <td>15.366197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>88.166667</td>\n",
       "      <td>18.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>87.554217</td>\n",
       "      <td>24.173290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>88.057685</td>\n",
       "      <td>26.332615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>84.920863</td>\n",
       "      <td>16.395683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>87.714286</td>\n",
       "      <td>24.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>83.666667</td>\n",
       "      <td>15.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>88.234043</td>\n",
       "      <td>28.061728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>87.225421</td>\n",
       "      <td>21.130532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>81.500000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>86.646589</td>\n",
       "      <td>27.048529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>87.250000</td>\n",
       "      <td>26.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>88.096154</td>\n",
       "      <td>25.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>87.818789</td>\n",
       "      <td>33.653808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US-France</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>84.600000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>84.478261</td>\n",
       "      <td>25.847059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           points      price\n",
       "country                                     \n",
       "Albania                 88.000000  20.000000\n",
       "Argentina               85.996093  20.794881\n",
       "Australia               87.892475  31.258480\n",
       "Austria                 89.276742  31.192106\n",
       "Bosnia and Herzegovina  84.750000  12.750000\n",
       "Brazil                  83.240000  19.920000\n",
       "Bulgaria                85.467532  11.545455\n",
       "Canada                  88.239796  34.628866\n",
       "Chile                   86.296768  19.344780\n",
       "China                   82.000000  20.333333\n",
       "Croatia                 86.280899  23.108434\n",
       "Cyprus                  85.870968  15.483871\n",
       "Czech Republic          85.833333  18.000000\n",
       "Egypt                   83.666667        NaN\n",
       "England                 92.888889  47.500000\n",
       "France                  88.925870  45.619885\n",
       "Georgia                 85.511628  18.581395\n",
       "Germany                 88.626427  39.011078\n",
       "Greece                  86.117647  21.747706\n",
       "Hungary                 87.329004  44.204348\n",
       "India                   87.625000  13.875000\n",
       "Israel                  87.176190  31.304918\n",
       "Italy                   88.413664  37.547913\n",
       "Japan                   85.000000  24.000000\n",
       "Lebanon                 85.702703  25.432432\n",
       "Lithuania               84.250000  10.000000\n",
       "Luxembourg              87.000000  40.666667\n",
       "Macedonia               84.812500  15.312500\n",
       "Mexico                  84.761905  29.095238\n",
       "Moldova                 84.718310  15.366197\n",
       "Montenegro              82.000000  10.000000\n",
       "Morocco                 88.166667  18.833333\n",
       "New Zealand             87.554217  24.173290\n",
       "Portugal                88.057685  26.332615\n",
       "Romania                 84.920863  16.395683\n",
       "Serbia                  87.714286  24.285714\n",
       "Slovakia                83.666667  15.333333\n",
       "Slovenia                88.234043  28.061728\n",
       "South Africa            87.225421  21.130532\n",
       "South Korea             81.500000  13.500000\n",
       "Spain                   86.646589  27.048529\n",
       "Switzerland             87.250000  26.500000\n",
       "Tunisia                 86.000000        NaN\n",
       "Turkey                  88.096154  25.800000\n",
       "US                      87.818789  33.653808\n",
       "US-France               88.000000  50.000000\n",
       "Ukraine                 84.600000  13.000000\n",
       "Uruguay                 84.478261  25.847059"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(values=['price', 'points'], index=['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'kotiki_horoshie'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'kotiki' + '_' + 'horoshie'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим на данные еще раз! Картинками :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Уменьшаем стресс и не идем знакомиться с бибилотеками для визуалиации, потому что...\n",
    "\n",
    "В библиотеке pandas есть инструмент для рисования! \n",
    "\n",
    "- df.plot() - метод для рисования\n",
    "\n",
    "Давайте попробуем  просто вызвать без всего и посмотрим, что выйдет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c8e49c50>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5fa48d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Не очень."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126b5dd7a20>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5f7f9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['price'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126cb55ee10>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5e9d3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что-то все не очень. Давайте попробуем добиться какой-нибудь разумной визуализации столбца points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c0783c18>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126cb753208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А что это новое за value_counts()? (Без него не советую запускать код выше, будет больно)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87     20747\n",
       "88     17871\n",
       "90     15973\n",
       "86     15573\n",
       "89     12921\n",
       "85     12411\n",
       "84     10708\n",
       "91     10536\n",
       "92      9241\n",
       "83      6048\n",
       "93      6017\n",
       "82      4041\n",
       "94      3462\n",
       "95      1716\n",
       "81      1502\n",
       "80       898\n",
       "96       695\n",
       "97       365\n",
       "98       131\n",
       "99        50\n",
       "100       24\n",
       "Name: points, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['points'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c0f9e198>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c8c226a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c8e67160>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c8f98c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].value_counts().plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опять некрасиво. Потому что у нас индексы не отсортированы при value_counts()!\n",
    "\n",
    "Поправим"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c8f98e10>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5e8df60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].value_counts().sort_index().plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126cb6d3a58>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c8ee44a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].value_counts().sort_index().plot(kind='area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c8ee4eb8>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5f669b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].sort_index().plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c8ebe128>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAE5xJREFUeJzt3X2sXdV95vHvE5wCZphizAWBwTVKrZSMU0h6hmGIQG0ghNKIN02n0EnqdgIeGiryImVCNNJ4JlVHoFIRVQOpHPCEtqmTFEKD8kLwoARGTUxynfBiMKkppMbYCjdjQhPBNDb5zR9nuTmyz7m+nG37+jrfj3R0zl5nrb3XlcDPXWuvfVeqCkmSXjPbHZAkHRwMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJaubNdgdejeOOO66WLFky292QpDlj/fr136+qiZnUnVOBsGTJEiYnJ2e7G5I0ZyT5h5nWdcpIkgQYCJKkxkCQJAEGgiSpMRAkScAMAiHJ6iTPJ9kwUHZskrVJNrX3Ba08Sf40yVNJHk3y5hHn/JUkj7V6f5ok++5Hkg6cJHu8pLlqJiOETwAX7lZ2PXB/VS0F7m/HAL8OLG2vFcDHRpzzY+37XXV3P7900Bv8x/+WW24ZWi7NJXsNhKp6ENi+W/ElwB3t8x3ApQPlf15964Bjkpw42LAd/8uq+nr19+/884H20pxTVbznPe/B7Wg11417D+GEqtoG0N6Pb+WLgGcH6m1pZYMWtfLp6vyzJCuSTCaZnJqaGrO70v4xODIYdizNJfv6pvKwsfLuvzbNpM5Pv6haVVW9qupNTMzo6WvpgLn22munPZbmknED4Xu7poLa+/OtfAtwykC9k4Gtu7Xd0sqnqyPNGUm49dZbvXegOW/cQLgHWN4+Lwc+N1D+O2210VnAi7umlnZpxz9MclZbXfQ7A+2lOWPwnsHgyMB7CZqrZrLsdA3wdeD1SbYkeTdwA/C2JJuAt7VjgC8CTwNPAR8H3jNwnocHTvv7wG2t3t8DX+r+o0gHXlXt8ZLmqr3+tdOqunLEV+cNqVvA0EnUqjpj4PMksGyGfZQkHQA+qSxJAubYfgjSwWbYjWSnjTRXOUKQxjQYBpdddtnQcmkucYQgdTQ4IjAMNJc5QpA6GBwZDDuW5pLMpfnOXq9X7qmsg8Wu0cCwEcJc+v9Kh7Yk66uqN5O6jhCkjpJw+eWXO12kOc9AkMY0OAq4++67h5ZLc4k3laUO/MdfhxJHCJIkwECQJDVOGUkd+KSyDiWOEKQxjVpV5GojzVWOEKSOfFJZhwpHCJIkoOMIIcl7gavp75P88ar6aJJPA69vVY4BfjC4F8JA2+8CPwReAXbO9Ek6SdL+MXYgJFlGPwzOBH4M3JvkC1X1WwN1/gR4cZrT/FpVfX/cPkgHA6eJdKjoMmV0GrCuql6qqp3AA8A//2Wvtl/yvwfWdOuidHAatZrIVUaaq7oEwgbg3CQLk8wHLgJOGfj+HOB7VbVpRPsC7kuyPsmKDv2QZo17KutQMvaUUVVtTHIjsBb4EfAIsHOgypVMPzp4S1VtTXI8sDbJk1X14O6VWlisAFi8ePG43ZUk7UWnVUZVdXtVvbmqzgW2A5sAkswDLgc+PU3bre39eeBu+vcihtVbVVW9qupNTEx06a4kaRqdAqH9dk+SxfQDYNeI4HzgyaraMqLdUUmO3vUZuID+FJQ0pyTZ4yXNVV0fTLsryUJgB3BtVb3Qyq9gt+miJCcBt1XVRcAJwN3tf555wF9V1b0d+yIdUNM9qey9BM1FnQKhqs4ZUf67Q8q20r/xTFU9DZze5drSwcInlXWo8EllSRJgIEiSGv+4ndSR00Q6VDhCkMbkk8o61DhCkDrwH38dShwhSJIAA0GS1BgIkiTAQJAkNQaCJAlwlZG0hwP5XIGrlHQwcYQg7WbYpjd7e/3Chz4/VjvpYGIgSJIAA0GS1BgIkiTAQJAkNV230Hxvkg1JHk/yvlb235I8l+Th9rpoRNsLk3wnyVNJru/SD0lSd2MvO02yDLgaOBP4MXBvki+0r2+uqpumaXsYcAvwNmAL8M0k91TVE+P2R5LUTZcRwmnAuqp6qap2Ag8Al82w7ZnAU1X1dFX9GPgUcEmHvkiSOuoSCBuAc5MsTDKf/n7Jp7Tv/iDJo0lWJ1kwpO0i4NmB4y2tTJI0S8YOhKraCNwIrAXuBR4BdgIfA14HnAFsA/5kSPNhj4IOfUonyYokk0kmp6amxu2uJGkvOt1Urqrbq+rNVXUusB3YVFXfq6pXquonwMfpTw/tbgs/HU0AnAxsHXGNVVXVq6rexMREl+5KkqbRdZXR8e19MXA5sCbJiQNVLqM/tbS7bwJLk5ya5OeAK4B7uvRFktRN1z9ud1eShcAO4NqqeiHJXyQ5g/4U0HeB/wSQ5CTgtqq6qKp2JvkD4MvAYcDqqnq8Y18kSR10CoSqOmdI2btG1N1K/8bzruMvAl/scn1J0r7jk8qSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1HTdQvO9STYkeTzJ+1rZHyd5MsmjSe5OcsyItt9N8liSh5NMdumHJKm7sQMhyTLgauBM4HTgHUmWAmuBZVX1y8DfAR+e5jS/VlVnVFVv3H5IkvaNLiOE04B1VfVSVe0EHgAuq6r72jHAOuDkrp2UJO1/XQJhA3BukoVJ5tPfL/mU3er8R+BLI9oXcF+S9UlWjLpIkhVJJpNMTk1NdeiuJGk688ZtWFUbk9xIf4roR8AjwK6RAUn+Szv+5IhTvKWqtiY5Hlib5MmqenDIdVYBqwB6vV6N219J0vQ63VSuqtur6s1VdS6wHdgEkGQ58A7gP1TV0H/Eq2pre38euJv+vQhJ0izpusro+Pa+GLgcWJPkQuBDwMVV9dKIdkclOXrXZ+AC+lNQkqRZMvaUUXNXkoXADuDaqnohyf8EDqc/DQT9G8/XJDkJuK2qLgJOAO5u388D/qqq7u3YF0lSB50CoarOGVL2iyPqbqV/45mqepr+UlVJ0kHCJ5UlSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQB3XdMe2+SDUkeT/K+VnZskrVJNrX3BSPaLm91NrUtNyVJs2jsQEiyDLia/l7IpwPvSLIUuB64v6qWAve3493bHgusBP5Na79yVHBIkg6MLiOE0+hvj/lSVe0EHgAuAy4B7mh17gAuHdL27cDaqtpeVS8Aa4ELO/RFktRRl0DYAJybZGGS+fS3xzwFOKGqtgG09+OHtF0EPDtwvKWVSZJmydh7KlfVxiQ30v/t/kfAI8DOGTbPsFMOrZisAFYALF68eIye6mfd6f/9Pl58ecd+v86S67+wX8//80e+lkdWXrBfr6GfbWMHAkBV3Q7cDpDkf9D/Tf97SU6sqm1JTgSeH9J0C/CrA8cnA18dcY1VwCqAXq83NDSk6bz48g6+e8NvzHY3OtvfgSN1XWV0fHtfDFwOrAHuAXatGloOfG5I0y8DFyRZ0G4mX9DKJEmzpNMIAbgryUJgB3BtVb2Q5AbgM0neDWwGfhMgSQ+4pqquqqrtSf4Q+GY7z0eqanvHvkiSOug6ZXTOkLL/C5w3pHwSuGrgeDWwusv1JUn7jk8qS5IAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJArpvkCMd9I4+7XreeMf1s92Nzo4+DWDubwWqg1enQEjyfvqb3hTwGPB7wFrg6FbleOAbVXXpkLavtDYAm6vq4i59kUb54cYb3FNZmoGxAyHJIuA64A1V9XKSzwBXDO6iluQuhu+pDPByVZ0x7vUlSftW13sI84Ajk8wD5gNbd32R5GjgrcDfdLyGJOkAGDsQquo54CZgM7ANeLGq7huochlwf1X944hTHJFkMsm6JHtMKUmSDqyxAyHJAuAS4FTgJOCoJO8cqHIlsGaaUyyuqh7w28BHk7xuxHVWtOCYnJqaGre7kqS96DJldD7wTFVNVdUO4LPA2QBJFgJnAiPvglXV1vb+NPBV4E0j6q2qql5V9SYmJjp0V5I0nS6BsBk4K8n8JAHOAza2734T+HxV/b9hDZMsSHJ4+3wc8BbgiQ59kSR11OUewkPAncC36C8ffQ2wqn19BbtNFyXpJbmtHZ4GTCZ5BPgKcENVGQiSNIs6PYdQVSuBlUPKf3VI2ST9Zxaoqq8Bb+xybUnSvuWfrpAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqXFPZf1MOBS2n/z5I187213QIc5A0CHvQOynvOT6LxwS+zbrZ5tTRpIkwECQJDUGgiQJMBAkSU2nQEjy/iSPJ9mQZE2SI5J8IskzSR5urzNGtF2eZFN7Le/SD0lSd2OvMkqyCLgOeENVvZzkM/S3zgT4YFXdOU3bY+nvtNYDClif5J6qemHc/kiSuuk6ZTQPODLJPGA+sHWG7d4OrK2q7S0E1gIXduyLJKmDsQOhqp4DbgI2A9uAF6vqvvb1HyV5NMnNSQ4f0nwR8OzA8ZZWtockK5JMJpmcmpoat7uSpL0YOxCSLAAuAU4FTgKOSvJO4MPALwH/GjgW+NCw5kPKath1qmpVVfWqqjcxMTFudyVJe9Flyuh84JmqmqqqHcBngbOralv1/RPwv4Azh7TdApwycHwyM59ukiTtB10CYTNwVpL5SQKcB2xMciJAK7sU2DCk7ZeBC5IsaCONC1qZJGmWjL3KqKoeSnIn8C1gJ/BtYBXwpSQT9KeFHgauAUjSA66pqquqanuSPwS+2U73kara3uHnkCR11OmP21XVSvrLRwe9dUTdSeCqgePVwOou15ck7Ts+qSxJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJTadASPL+JI8n2ZBkTZIjknwyyXda2eokrx3R9pUkD7fXPV36IUnqbuxASLIIuA7oVdUy4DDgCuCTwC8BbwSOZGCXtN28XFVntNfF4/ZDkrRvdNpCs7U/MskOYD6wtaru2/Vlkm8AJ3e8hiTpABh7hFBVzwE3AZuBbcCLu4XBa4F3AfeOOMURSSaTrEty6ajrJFnR6k1OTU2N211J0l50mTJaAFwCnAqcBByV5J0DVW4FHqyq/zPiFIurqgf8NvDRJK8bVqmqVlVVr6p6ExMT43ZXkrQXXW4qnw88U1VTVbUD+CxwNkCSlcAE8IFRjatqa3t/Gvgq8KYOfZEkddQlEDYDZyWZnyTAecDGJFcBbweurKqfDGuYZEGSw9vn44C3AE906IskqaMu9xAeAu4EvgU81s61Cvgz4ATg621J6X8FSNJLcltrfhowmeQR4CvADVVlIEjSLOq0yqiqVgIrZ3LOqpqkLUGtqq/RX5YqSTpI+KSyJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkoGMgJHl/kseTbEiyJskRSU5N8lCSTUk+neTnRrT9cJKnknwnydu79EOS1N3YgZBkEXAd0KuqZcBhwBXAjcDNVbUUeAF495C2b2h1/xVwIXBrksPG7YskqbuuU0bzgCOTzAPmA9uAt9LfWhPgDuDSIe0uAT5VVf9UVc8ATwFnduyLJKmDLnsqPwfcBGymHwQvAuuBH1TVzlZtC7BoSPNFwLMDx6PqSZIOkLH3VE6ygP5v+qcCPwD+Gvj1IVVrWPMZ1iPJCmAFwOLFi8fqq/RqJMP+85xBuxtffZuqof/ZS7Oiy5TR+cAzVTVVVTuAzwJnA8e0KSSAk4GtQ9puAU4ZOB5Vj6paVVW9qupNTEx06K40M1V1wF7SwaRLIGwGzkoyP/1fqc4DngC+Avy7Vmc58Lkhbe8BrkhyeJJTgaXANzr0RZLUUZd7CA/Rv3n8LeCxdq5VwIeADyR5ClgI3A6Q5OIkH2ltHwc+Qz9A7gWurapXOvwckqSOMpeGrb1eryYnJ2e7G5I0ZyRZX1W9mdT1SWVJEmAgSJIaA0GSBBgIkqTGQJAkAXNslVGSKeAfZrsf0hDHAd+f7U5IQ/xCVc3oqd45FQjSwSrJ5EyX9kkHK6eMJEmAgSBJagwEad9YNdsdkLryHoIkCXCEIElqDASpgyS3tT3Cp6tz6d7qSAcDp4yk/SzJJ4DPV9Wde6srzSZHCNKAJEuSPJnkjiSPJrmzbQJ1XpJvJ3ksyeokh7f6X03Sa59/lOSPkjySZF2SE5KcDVwM/HGSh5O8Lsl1SZ5o5//UbP680iADQdrT64FVVfXLwD8CHwA+AfxWVb2R/l7kvz+k3VHAuqo6HXgQuLqqvkZ/h8APVtUZVfX3wPXAm9r5r9nvP400QwaCtKdnq+pv2+e/pL897DNV9Xet7A7g3CHtfgx8vn1eDywZcf5HgU8meSewc5/0WNoHDARpT+PeWNtRP70p9wr9kcQwvwHcAvwKsD7JqHrSAWUgSHtanOTfts9XAv8bWJLkF1vZu4AHXsX5fggcDZDkNcApVfUV4D8DxwD/Yp/0WurIQJD2tBFYnuRR4FjgZuD3gL9O8hjwE+DPXsX5PgV8MMm3gaXAX7bzfBu4uap+sE97L43JZafSgCRL6C8RXTbLXZEOOEcIkiTAEYIkqXGEIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNf8fvvNq3v/f21wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126cb8b2278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['points'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ну вроде про один столбец разобрались. А как красиво сразу про все столбцы нарисовать?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126cb5bb5c0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c8dfc1d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind ='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "points       AxesSubplot(0.125,0.125;0.352273x0.755)\n",
       "price     AxesSubplot(0.547727,0.125;0.352273x0.755)\n",
       "dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c0fc9b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='box', subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А совместно как распределены цены и оценки сомелье?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126b68e8128>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b6b8b278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " data.plot(x='points', y='price', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "      <th>Bordeaux-style Red Blend</th>\n",
       "      <th>Cabernet Sauvignon</th>\n",
       "      <th>Chardonnay</th>\n",
       "      <th>Pinot Noir</th>\n",
       "      <th>Red Blend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>18.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>82</td>\n",
       "      <td>72.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   points  Bordeaux-style Red Blend  Cabernet Sauvignon  Chardonnay  \\\n",
       "0      80                       5.0                87.0        68.0   \n",
       "1      81                      18.0               159.0       150.0   \n",
       "2      82                      72.0               435.0       517.0   \n",
       "\n",
       "   Pinot Noir  Red Blend  \n",
       "0        36.0       72.0  \n",
       "1        83.0      107.0  \n",
       "2       295.0      223.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_counts = pd.read_csv('dpo_1-2_top-five-wine-score-counts.csv')\n",
    "wine_counts.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126b21a3dd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126b5e25b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_counts.plot.line(x='points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_counts.plot.area(x='points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_counts.plot.bar(x='points', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
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